Patient Risks and Clinical Care Disruptions in Rural Hospital Mergers

Patient Risks and Clinical Care Disruptions in

Rural Hospital Mergers

Section 1: Foundation of the Study and Literature Review

Introduction

The world of the U.S. healthcare system is experiencing many wave changes,

among them the trend for rural hospitals to be taken over by sizeable hospitals that are

part of larger healthcare systems (Henke et al., 2016; Rural Health Reform Policy

Research Center (RHRPRC), 2014). The broad pattern of large health systems acquiring

and consolidating rural hospitals may contribute to increased rates of mortality, and

hospitalizations for rural patients with conditions, such as cancer, asthma and pneumonia,

which are preventable if patients receive the appropriate and convenient quality of care

(Hass, Gawande, & Reynolds, 2018). The absorption of rural health systems also may be

at least partially responsible for the higher incidence of other chronic health conditions,

such as hypertension, heart disease and emphysema, among rural residents (Holmes,

2015). A recent collaborative study by researchers from the Harvard-Affiliated Health

Systems identified hospital mergers and affiliations as an emerging area of patient risks

emanating from critical sources such as new patient populations, unfamiliar infrastructure

and unique setting for clinicians (Hass et al., 2018) While there are no indications that the

pace of hospital and health system alliance and acquisitions will diminish, there has been

little attention paid to patient safety in rural hospital mergers (RHMs), which account for

up to roughly half of all U.S. hospitals (Brown et al., 2016).

Since the execution of the Affordable Care Act of 2010, there has been a quick

surge of hospital consolidations- mergers and acquisitions (Pope, 2014). A recent analysis

by Hall (2018) revealed that 30 hospital and health system partnership transactions were

completed in the first quarter of 2018, which is up more than 11% from the first quarter

of 2017. Williams Jr, Thomas, Howard, and Pink (2018) described the number and

geographic distribution of 380 RHMs from 2005 through 2016 in over 24 U.S. states as

significant, with potential concerns fueled by cost pressures, technological advances, and

patient demands.

Although such trends are substantial, they do not capture the full extent or impact

of the affiliations of rural health systems on the quality of care after such mergers have

occurred. As U.S. rural healthcare continues to transform across the country, the North

Carolina (NC) RHRPRC (2014) predicted that over 700 more hospitals that are rural are

at risk of either being taken over or closed within the next 10 years. Despite playing the

leading role in the provision of various types of health services to 60 million U.S.

citizens, hospitals in the rural United States face a unique set of challenges and are often

coerced to make difficult decisions where patient safety may be compromised (American

Hospital Association [AHA], 2018).

Advocates for hospital mergers often present one side of the argument: alliances

are necessary to lower costs and improve efficiency and access to care. Noether and May

(2017) reported that when healthcare organizations begin planning about mergers or

acquisitions, network development leaders — whose experience is business oriented and

not in medicine — tend to be at the forefront of the dialogues. Perhaps, healthcare

executives with little proficiency in patient safety are customarily responsible for

executing healthcare mergers and acquisitions (Haas et al., 2018). Because the incentive

is often financial rather than clinical, the goals and responsibilities for patient safety and

quality of care are often overlooked (see., Haas et al., 2018; Kaufman et al., 2016).

Nevertheless, system expansions or mergers can increase market power and broaden the

capacity of health services to patients, at least in the short term; however, without

planning, such changes can cause significant patient risks and may also create a set of

other challenges such as increases in medical malpractice, delays in treatments, fines, and

penalties (Johar, & Savage, 2014). Although healthcare organizations have invested vast

sums of money in mergers and acquisitions, the quality of care provided after such

consolidations frequently falls far short of what is optimal (Noles, Reiter, Sheps, &

Boortz-Marx, 2015).

By focusing on four aspects of leveraging organizational synergies: (i.e. change

management, governance mechanisms, system integration best practices, and the role of

clinical leadership), I sought to add the knowledge needed to promote the adoption of the

patient safety culture model in RHMs. This investigation was anticipated to fill a gap in

the literature by exploring the correlational of healthcare transformation changes in

structures, processes, organizational culture, and values on clinical outcomes for rural

patients. Trying to discern the relationship between rural communities, and hospital

mortality through conducting a performance comparison of RHs post mergers could

prompt policymakers to identify strategies for future quality improvement efforts that

narrow identified gaps. Such specifics would not only allow policymakers to better

prioritize the best integration practices likely to improve quality of care but would begin a

new era towards a more comprehensive redesign of better care pathways that would

reduce mortality rates in rural communities.

In this study, I investigated the aspects of patient risks and CCDs in RHMs. While

different scholars have studied issues of patient risks and clinical discourse, not much

attention has been placed on how RHMs contribute to the patient risks and clinical

discourse. This first section of this study comprises the introduction, background to the

problem statement, purpose of the study, significance of the study, theoretical foundation

for the study, definition of terms, nature of the study, secondary data types and sources of

information, a literature review related to critical variables and/or concepts, definitions,

research questions and associated hypotheses, assumptions, scope and delimitations, and

a summary.

Background of the Study

Unsafe healthcare is always one of the challenges of the U.S. healthcare delivery

system and a well-recognized health issue, usually listing among the top three causes of

deaths in the United States, with rural residents experiencing more significant hardship

(NC RHRPRC, 2014; Taylor, 2019). As the broad trend of large health systems acquiring

and consolidating rural hospitals is expected to continue, serious questions about the

impact the mergers will have on access to high-quality health care services in U.S. rural

communities have been raised (Hass et al., 2018). Infrastructure changes and

organizational complexities from healthcare system mergers not only can challenge

clinicians but can cause risk to patients (Noles et al., 2015). The idea that merging

healthcare systems and processes may be deceptive and yield medical errors and harm

patients is not a new phenomenon.

Recent estimates suggest that approximately 97% of U.S. landscape belongs to

rural counties, and 60 million people (roughly 19.3% of the population reside in these

areas (U.S. Census Bureau, 2016). According to the Centers for Disease and Prevention

Control (CDC), an estimated 40% of rural residents reported missing a usual source of

care, of which 15% indicated travel to the next hospital or financial reason for requiring

regular access to quality of care (CDC, 2017). These numbers are also elevated among

racial and ethnic minority groups compared with White populations (CDC, 2013).

Among the crisis of access, equity, economic complexities, and shortages of providers, a

disproportionate number of rural hospitals across the United States are modified through

mergers and acquisitions (NC RHRPRC, 2014; Noles et al., 2015).

While numerous proposals to improve the U.S. rural delivery services have been

initiated, substantial differences across a range of quality domains remain for rural

residents compared with urban and suburban counterparts (Ely, & Hoyert, 2018). Even

though previous studies have shown some anticipated benefits of hospital mergers (e.g.

improved financial performance, service consolidation, and operating efficiencies), there

has been less progress in improving quality, and reducing health inequities and mortality

rates (Noles et al., 2015).

Even though much is known about the financial influence of health system

mergers and acquisitions, there has been little evaluation of the CCDs and patient safety

risks (PSRs) that hospital mergers and acquisitions have on rural patients. The recent

decline in life expectancy, the recent increase in chronic disease deaths, and the

widespread of other diverse chronic disease morbidity and mortality in the rural United

States, with substantial differences in quality domains, raise the issue of whether RHMs

are addressing the health of its rural population effectively (Crossman et al., 2017; Health

Resources & Services Administration, 2017; Kessler, & Alverson, 2013).

Problem Statement

In the United States, approximately 60 million people live in rural communities,

including millions of Medicare and Medicaid beneficiaries (AHA, 2018). Nearly 1 in 5

U.S. citizens who live in rural areas rely heavily on rural hospitals as the cornerstone of

their health system during emergencies and disasters (Abuse & Administration, 2016).

Because such hospitals care for most seniors with complex chronic diseases, such as

diabetes, cancers, strokes, and lower respiratory complications, these institutions

encounter regulatory and financial pressures that increase vulnerability to cutbacks and

patient safety risks (CDC, 2017). The American Academy of Family Physicians (2015)

noted several challenges that rural U.S citizens face, including living in communities with

disproportionally higher famine rates, having more chronic conditions, being uninsured,

experiencing a disintegrated healthcare delivery system with a shrinking health

workforce, and lacking access to high-quality health care services.

The number of RHMs has increased significantly in recent years. This broad

pattern of large health systems acquiring and consolidating rural hospitals is expected to

continue, raising questions about the impact the mergers will have on access to

highquality health care services in rural communities. According to Kaufman (2018),

healthcare organizations announced a total of 115 transactions in 2017, representing a

13% surge compared to the previous year. From 2005 to 2016, more than 380 rural

hospitals in the United States were merged, and the activities have affected the delivery

and availability of services as joining hospitals work toward greater efficiency in cost

control (Williams Jr et al., 2018).

As the rural healthcare system is abruptly consumed through mergers, the

aftershock creates permanent gaps in access to quality of care between rural and urban

U.S. citizens (Kaufman et al., 2016; Worek, 2017). Numerous studies have highlighted

that rural patients with complex chronic illnesses have shown worse patient outcomes

than their urban counterparts (CDC, 2013; Unger et al., 2018). McCullough and Flowers

(2018) noted inferior mortality and life expectancy rates for the rural United States, with

the most substantial disparities gap noticed in access to quality of care and patient safety

risks. Noles et al. (2015) reported that high inpatient mortality in state hospitals in

California was positively associated with hospital mergers. Previous studies have shown

that there is the potential for both positive and negative effects on patients, communities,

and employees (Johar, & Savage, 2014; Noles et al., 2015). However, when health

systems integrate, the work processes and organizational culture; recruitment structure of

physicians, nurses, and other healthcare providers, as well as the overall delivery of

healthcare within the merged entity, changes (Vogel, & Hadfield, 2017).

Increasing evidence suggests that 440,000 U.S. citizens are dying annually from

the poor quality of care in the hospital settings, and rural inhabitants experience more

significant health threats than any other group in the United States (James, 2013). Moy,

Garcia and Bastion (2017) expressed that preventable deaths were about 50% higher in

rural areas, partly because of higher risk due to lengthy trips to specialty and urgent care

or other social, demographic, and economic factors (see Liu, Singer, Sun, & Camargo,

2011). Adding to these statistics, the U.S. National Vital Statistics Systems [NVSS]

(2017) reported that the newborn death rate is more than 25% higher in rural communities

compared to metropolitan areas. Rural U.S. citizens are more likely to die from

potentially preventable health threats than their urban counterparts (CDC, 2017). The US

Census Bureau (2016) further asserted that there had been significant rural-urban health

disparities in mortality rates, life expectancy, the incidence of diseases, and morbidities.

The noticeable gap in health between rural and urban U.S. citizens shows that lower

quality of care is one of the most pressing patient safety concerns, specifically in RHMs,

that is demanding more leadership and action (NC RHRPRC, 2014).

Patient risks and CCDs could be the probable foremost reason for the discrepancy

in both adult and infant mortalities in rural areas (U.S. NVSS, 2017). The exact scope of

the problem is displayed by the fact that more than 65%, which is approximately 286,000

deaths each year in rural communities, are entirely preventable (Agency for Healthcare

Quality and Research [AHQR], 2018; Ely et al., 2018). Arguably, the noticeable gap in

health between urban and rural communities is not something new, particularly to the

U.S. healthcare system (Moscovice, Johnson, & Burstin, 2017). However, various

researchers have highlighted this worsening gap between health outcomes in urban and

rural inhabitants of the United States (James et al., 2017; Noles et al., 2015; Unger et al.,

2018). Nevertheless, between 2005 and 2016, more than 380 RMH transactions have

been completed nationwide, with little data reported on the clinical impact of the mergers,

or the impact on access to care within rural communities. Although much is known about

the financial effects of hospital mergers, research shows partnerships and transactions

might create a permanent gap, which leaves perpetual patient risks and clinical

disruptions (American’s Health Insurance Plans, 2014; American Health

Information Management, 2012; Hayford, 2011; Jennings, 2008; Johar et al., 2014;

Noether, & May, 2017). Among other challenges, such as long distances between health

care facilities and trauma centers; cost of access to specialized care, and patient

risksrelated to changes in patient populations, infrastructure, and clinical practice settings,

the responsibility for safety and quality when entities merge may be unclear (Capps,

Dranove, & Ody, 2018; Haas et al., 2018; Henke et al., 2016). Perhaps, the interruption

induced by integrating two independent facilities may negatively impact quality,

particularly in the immediate aftermath of the merger (Hayford, 2011).

Purpose of the Study

In this study, I employed a quantitive approach to describe patient risks and

disruptions of clinical care in RHMs. The principal objective of this correlational study

was to examine the potential association of healthcare transformation change, in

structures, processes, organizational culture, and values on clinical outcomes for rural

patients during mergers. A secondary goal of the study was to enhance the understanding

of the incidence of patient risks and clinical turbulence after mergers as a basis to reduce

them. To date, while many studies have focused on the association between hospital

consolidation and the financial aspects, no previous studies have been conducted to

explore or identify particular areas where system complexities handicap or delay the

adaption of a holistic patient safety culture in RHMs (Noles et al, 2015). To bridge this

gap in the published literature, I focused on three aspects of leveraging organizational

synergies: governance mechanisms, system integration best practices, and the role of

clinical leadership.

Using the Medicare Mortality Ratings Databases, Hospital Compare, and Hospital

Evaluation Databases, which tracked mergers in rural hospitals in the United States from

20102018, in this study I specifically assessed the performance of rural hospital alliances

regarding structures, processes, and outcomes and evaluated the correlation between

quality domains. I used the patient outcome measures of mortality and readmission rate to

study the effects of hospital alliances on quality of care. Understanding the relationship

between hospital mortality and the potential performance comparisons of rural hospitals

post mergers could prompt policymakers to identify opportunities for future quality

improvement efforts that narrow identified gaps.

Significance

I anticipated that the findings of this study would fill a gap in the literature by

focusing on the relationship of healthcare transformation changes in structures, processes,

organizational culture, and values on clinical outcomes for rural patients. Trying to

understand the relationship that exists between rural communities and hospital mortality

through conducting a performance comparison of RHs post mergers could prompt

policymakers to identify opportunities for future quality improvement efforts that narrow

identified gaps. By defining quality improvement initiatives and, facilitating this kind of

feedback and discussion, the findings of this study may present healthcare legislators with

potential tools to highlight low points in patient care of which the U.S. healthcare system

may not have previously been aware (see Institute of Medicine [IOM], 2012;

Manghani, 2011).

The results of this study could make substantial contributions to the field of health

care administration and may result in significant social change through recognizing rural

the population as a separate group in research, analyzing patient risks impacting this

group and comparing them with other factors that attribute to the disparity of accessing

quality of care and mortality rates, which will significantly remedy clinical challenges

and improve patient safety (see Ackerman et al., 2018; James et al., 2017; Khalil et al.,

2017). Various network developmental leaders, practitioner administrators, and

policymakers could look at the findings of this research study to identify effective

integration practices for hospital mergers. In addition to achieving better quality of care

and minimizing preventable deaths, improvement in clinicians’ engagement in hospital

consolidations may sustain continuity of care and provide more opportunities for

financial stability in the rural healthcare system.

Framework of the Study

The conceptual framework for this study was grounded on Donabedian's (2005)

theory of epistemological perspective. According to Donabedian's epistemological

analysis for quality management, improvements in the structure of care should lead to

improvements in clinical processes that should improve patient outcomes (Moore,

Lavoie, Bourgeois, & Lapointe, 2015). I used Donabedian’s quality improvement model

to assess the performance of rural hospitals post mergers regarding structure, process, and

outcome and evaluate the correlation between quality domains. The structure includes all

the factors that affect the context in which care is delivered, the process describes the sum

of all actions that make up healthcare, and outcomes denote all the effects of health care

on patients or populations. The goal of this study was to further explore the principles for

successful health systems integration in critical areas such as restructuring, organizational

flexibility, and adaptation (see Grol et al., 2007; Suter, Oelke, Adair, & Armitage, 2009).

Application of Donabedian’s (2005) philosophical theory to evaluate and

transform the social conditions of the targeted rural hospitals as well as testing the

processes in place that protect patients and procedures of hospital cultures involved,

clearly supports that the method used strongly affect the conclusions to be drawn. Given

the inherent complexities of the U.S. healthcare system, a theoretical framework that

requires a holistic approach to identify specific areas where system complexities slow or

inhibit progress could prove beneficial to a study (see Hempel, Gibbons, & Ulloa, 2015;

IOM, 2011). Donabedian’s theoretical work offered an in-depth description process

through which I could articulate a definite need for quality improvement initiative to

ensure the level of readiness and adaptability necessary for quality patient safety and

provide a useful outlet for minimizing CCDs. Further, the theory may provide

decisionmakers with system-level indicators of quality and assist in exploring strategies

that network development leaders may use to improve organizational performance.

Definitions

Clinical care disruptions/Clinical Discourse (CCDs): Health care services

characterized as turbulent, hectic conditions, more interruptions, and distractions resulting

from the rapid growth of large health care corporations, which has altered organizational

structures and dynamics as well as continually changed health policies (see Jennings,

2008).

Clinical leadership measures: Measures that reflect the attributes of ensuring that

the values and cultures of two different entities are aligned with the common goal of

improving the quality of care (see Sarto, & Veronesi, 2016).

Hospital mergers/consolidations: The concept of bringing together individual

hospitals via complete changes in the actual ownership. Combining administrative and

organizational resources of two or more health care facilities can occur through either

merger or acquisition (Hill Jr., 2018).

Hospital structures-care processes: These processes consist the way healthcare

systems and processes work to achieve the desired outcome. This may include the length

of time a patient waits for a clinical audit, ensuring required standards of care are met, or

making sure that staff members wash their hands and report incidents (AHRQ, 2011).

Governance Mechanisms: Measures that reflect strategic controls, policies, and

guidelines that drive the organization toward its objectives and further demonstrates the

relationship between the characteristics of the health systems that have merged and the

leadership processes and their consequences to the health and welfare of individuals and

the community (Baker, Denis, Pomey, & MacIntosh-Murray, 2010).

Patient Safety Risks (PSRs): Hazards that cause or can cause healthcare-associated

harm or injury (Jennings, 2008).

Preventable Adverse Events (PAEs): Care that fell below the standard expected of

physicians in their community (see Barach, Jacobs, Lipshultz, & Laussen, 2015); Hodges,

Spiller, Casavant., Chounthirath, & Smith, 2018).

Quality domains: The IOM defines quality domains as care-oriented task that

encompasses the following key indicators of care -safety effectiveness, patient

centeredness, efficiency, timeliness, and equitable (AHRQ, 2016).

Rural Hospitals (RHs): These are hospitals that are found within the rural

localities, far away from towns (NC RHRPRC, 2014).

System integration best practices measures: Measures that helps organizations to

identify unintended consequences of change known as organizational balancing

measures. (Suter, Oelke, & Armitage, 2009)

Research Questions and Hypotheses

In this study, I developed the following four research questions and corresponding

hypotheses about the association between organizational synergy ramifications, PSRs,

and CCDs post RHMs as determined by Donabedian’s model of care to assess the

outcomes of quality of care in rural health centers (see Lukas et al., 2008).

RQ 1: Is there an association of healthcare transformation changes in structures,

processes, organizational culture, and values on clinical outcomes for rural

patients?

H01 —There is no association of healthcare transformation changes in

structures, processes, organizational culture, and values on clinical

outcomes for rural patients.

Ha1 —There is an association of healthcare transformation changes in

structures, processes, organizational culture, and values on clinical

outcomes for rural patients.

RQ2: What is the relationship between governance mechanisms and patient

outcomes in RHMs?

H02 —There is no relationship between governance mechanisms and

patient outcomes in RHMs.

Ha2 —There is a relationship between governance mechanisms and

patient outcomes in RHMs.

RQ3: How do best practices facilitate quality-focused initiatives and contribute to

eliminating unintended patient risks associated with structural changes and the

process of care?

H03—Best practices do not facilitate quality-focused initiatives and do not

contribute to eliminating unintended patient risks associated with

structural changes and the process of care.

Ha3 —Best practices facilitate quality-focused initiatives and contribute to

eliminating unintended patient risks associated with structural changes and

the process of care.

RQ4: What is the relationship between clinician leadership and the quality of care

outcomes in RHMs?

H04— Clinical leadership in RHM and acquisition deals is not a

significant predictor of quality of care outcomes.

Ha4 —Clinical leadership in RHM and acquisition deals has a significant

relationship with the quality of care outcomes.

Nature of the Study

In this study, I conducted a secondary data review in which relevant data were

collected to determine patient risks and CCDs in RHMs. The focus was to confirm or

validate existing relationships and yield generalization that could be applied in the

development of some critical theories. Trying to understand the link between rural

communities and hospital mortality through conducting a performance comparison of

RHs postmergers could prompt policymakers to identify opportunities for future quality

improvement efforts that narrow identified gaps. In this study, I gathered data from

Medicare Mortality Ratings Databases, Hospital Compare, and Hospital Evaluation

Databases from 20102018 to research the performance differences. The data collected

from the secondary documents were entered into Statistical Package for the Social

Sciences (SPSS) computer software and analyzed as considered appropriate. The specific

variables investigated were RHMs (i.e., the independent variables) and patient risks and

clinical disruptions (i.e., the dependent variables).

Literature Review Related to Key Variables and/or Concepts

Several scholars have conducted studies on the topic of patient risks and CCDs in

RHMs. In this literature review, I considered the implications of care fragmentation that

dominate the more obvious health care crisis and the need for integrative solutions in

hospital mergers (see Stange, 2009). To begin with, Noles et al. (2015) and Burkey,

Bhadury, Eiselt, and Toyoglu (2017) highlighted and studied the characteristics of RHs

that closed, merged, or were acquired to see how they coped with structural changes,

what they have in stock, and what other predictors of success or failure related to access

to care. Mainly, Burkey et al. discussed the effects of hospital closures on equity and

examined whether rural residents are disproportionately affected by hospital closures.

Noles et al. used data from Irving Levin Associates (ILAs)' Healthcare Merger, and

Acquisition Report and Medicare Cost Reports from 20052012. The researchers then

applied logistic regression to identify specific factors that tend to predict mergers, while,

at the same time, using multiple regression to examine different hospital measures after

mergers and acquisitions.

Their empirical findings cited the variations in patient needs, demographic shifts, and

facility conditions as reasons for closures. Noles et al. further identified changes in rural

hospital financial performance, staffing levels, and inpatient services as predictive factors

of mergers; however, the methodology they used was not apparent.

Hass et al. (2018) cited three factors that can create significant downstream

conditions that can result in multiple failures. They highlighted the need for healthcare

leaders to use system data to plan for unexpected downstream situations driven by

changes in both organizational and governance processes. However, this information was

based on an individual opinion without any form of scientific evidence.

Hospital mergers are paradigmatic complex entities, and their ability for

collaboration, commitment, and leadership among clinicians can be obstructed by culture

dynamics, value boundaries, and changing government regulations. Grol, Bosch,

Hulscher, Eccles, and Wensing (2007) offered different theoretical assumptions to plan

for and manage the impact of quality improvement initiatives in complex changing

environments, and these were possible theories considered for application in the present

study. Grol et al. discussed the theories of leadership in management and total quality

management, often known as continuous quality improvement, as tested mechanisms

when redesigning multidisciplinary care processes and promoting a quality safety culture

in a changing environment. Millar, Mannion, Freeman, and Davies (2013), on the other

side, elucidated the role of a hospital board’s oversight of patient safety and revealed

several approaches to be explored in diffusing the complexities and ambiguities related to

evidence-informed governance and quality practices in changing structural environments.

Brown et al. (2016) presented different views on the successful implementation of

clinical networks about the reorganization of healthcare services in hospital

consolidations. Johar et al. (2014) and Kaufman et al. (2016) cited problematic

downstream conditions of patient risks attributable to the rapid surge of RHMs and

affiliations. Both groups of authors offered unique insights into patient wait time effects

and variations in hospital performances.

Experts of healthcare mergers and acquisitions argued that structure changes

involving cultural assimilation, process, integration, technology, and system integration

are essential to survive in the shifting industry landscape. (see Jennings, & Hughes,

2008). Williams Jr. et al. (2018), on their part, examined the pattern of mergers of rural

hospitals across the United States. from 20052016 and looked at the financial impact of

merging and discussed the effect on access to care within rural communities. The authors

also identified significant concerns such as reduced negotiating power with insurers,

centralized governance, and outsourcing of support services as factors that could create

an unnecessary burden to clinicians and confusion among patients. Ackerman et al.

(2018) examined proposed patient safety measures for capturing patient safety variations

in ambulatory care in safety net health systems (SNHS). The authors reported the

problems faced by SNHS, rural hospitals, and other providers, and found that lack of

active participation of key stakeholders such as patients, clinicians, staff, data system

professionals, and health system leaders, remains an impediment to quality improvement

initiatives.

Secondary Data Types and Sources of Information

Data in this study were based on information collected from the 50 states, and are

publicly available on Medicare Hospital Compare. Mortality and hospital readmission

data on specific medical conditions and patient characteristics, which covers all 50 states

excluding the District of Columbia, were included in the study. Patient morbidities and

ethnicity information came from the Centers for Medicare and Medicaid Services (CMS)

Hospital General Information File (i.e., HOSArchive_Revised_Flatfiles_20190702). Data

on hospital characteristics were obtained from NC RHRPRC and ILAs Healthcare

Mergers & Acquisition reports collected from 2010 through 2018, in which I identified a

total of 380 rural hospitals that have merged or acquired. Both NC RHRPRC and ILAs

maintains a publicly available map for tracking and counting RHMs and closures.

Additional data for hospital characteristics were collected from the Medicare Provider of

Service files and the Healthcare Cost Report Information System.

I assessed data from Hospital Consumer Assessment of Healthcare Providers and

Systems measures publicly reported on Hospital Compare to measure hospital

performance on clinical process and patient experience composite measures (Casey,

Hung, Evenson, Distel, & Moscovice, 2015). The performance composite measures

consisted of routinely collected CMS outcome measures were grouped into six categories

weighted by importance: mortality, safety of care, rehospitalizations, patient experience,

effectiveness of care, and timeliness of care. To minimize the susceptibility in these

measures of quality, I grouped the six separate outcome measures followed by CMS into

three composite outcome indices: one for 30-day mortality, one for 30-day readmission,

and one that combines both mortality and readmission measures.

My digital procedure of collecting secondary data from scholarly databases was

comprised of entering specific words, such as patient risks, clinical care disruptions, and

rural hospital mergers. I only referenced websites that showed relevant results, or

conventional sources of secondary data for social sciences such as national censuses,

information collected by government departments, and other organizational records

related to the topic under study. The exclusion and inclusion criteria for this review of the

literature was only journals written in English and those not published more than 7 years

ago being considered for analysis. By performing secondary data analysis, I adequately

gathered relevant information related to specific patient risks and CCDs in RHMs.

For this study, I assessed secondary data collected by government public services

departments, libraries, and censuses, such as the U.S. Census, the CDC, the CMS and the

U.S. NVSS. The sources were obtained through the use of search engines that enabled

access to different articles plus some other sources of information that were needed for

the study. The search engines, in this case, did allow access to academic essays and

journals, e-books, and papers that contained content on the topic. Relevant articles and

ebooks were also reviewed after first gaining access to the relevant abstracts and articles,

through entering the keywords that were selected for the study. Upon acquiring enough

resources and materials, I checked the information, data, and records deemed relevant.

Assumptions

In this study, I assumed that RHMs have both positive and negative patient risks

and CCDs. I also assumed that relevant data would be obtained as far as the topic is

concerned. Another assumption that the data collected from Medicare Mortality Ratings

Databases, Hospital Compare, and Hospital Evaluation Databases on PSRs were reported

precisely and correctly, and the use of the current studies was critical to drawing

significance findings.

Scope and Delimitations

I constructed this study to analyze the relationship which is there between rural

communities and hospital mortality through conducting a performance comparison of

RHs post mergers with the desire to identify opportunities for future quality improvement

efforts that narrow identified gaps. The extent of the study was limited to patient risks and

clinical discourse in RHMs. In this study, I conducted a quantitative analysis in which

relevant, quantitative, and secondary data from Medicare Mortality Ratings Databases,

Hospital Compare, and Hospital Evaluation Databases were collected to determine

patient risks and clinical discourse in RHMs.

Limitations, Challenges, and Barriers

Various studies have reported the problems faced by researchers when selecting

their research methodologies. As outlined by Saunders, Lewis, and Thornhill (2009), the

research methodology serves as the foundation of the study. With the use of the

quantitative methodology, there were at least three potential challenges that could have

resulted in limitations that may have been beyond my control. The lack of resources for

data collection may have resulted in limited understanding and, especially, the resources

needed to perform an in-depth quantitative investigation (see Younus, 2014). Quantitative

study demands comprehensive statistical analyses, which may be challenging to perform

for researchers from non- mathematical backgrounds. Finally, to achieve in-depth quality,

a quantitative study may involve extra time, investment, and resources to refine the

results (Saunders et al., 2009). To reduce some of the barriers, I used publicly available

secondary data from Medicare Mortality Ratings Databases, Hospital Compare, and

Hospital Evaluation Databases (2010–2018) to research the patient risks and CCDs in

RHMs. Due to its exposure and public cross-examination, secondary data collected from

Medicare Mortality Databases and Hospital Compare, and Hospital Evaluation Databases

have a preestablished degree of validity and reliability.

Significance, Summary, and Conclusion

In this first section of the study, I introduced the study, and provided the

background to the problem, the purpose of this study, the theoretical framework, the

nature of the study, a literature review related to critical variables, secondary data types

and sources of information, research questions with associated hypotheses, assumptions,

scope and delimitations, limitations, and challenges and barriers. Safeguarding patients in

health care settings is cornerstone to achieving high-quality health care for the U.S.

population (IOM, 2001; Sacristan, 2013). Rural inhabitants of the United States are

approximately 50% more likely to die from preventable causes (James, 2013). Rural

communities still experience a newborn death rate 25% higher than their urban

metropolitan counterparts (Moy et al., 2017; NVSS, 2017). Many of these PAEs are

linked to the poor quality of care (CDC, 2017).

Evaluating these risk factors and identifying the national goals that aim to increase

patient safety through best practices focused on healthcare-associated adverse events in

RHMs could generate improved clinical outcomes (Darker, Nicolson, Carroll, & Barry,

2018). Hence, understanding the relationship between rural communities and hospital

mortality through conducting a performance comparison of RHs postmergers could

prompt policymakers to identify opportunities for future quality improvement efforts that

narrow identified gaps.

Although numerous proposals to improve the U.S. rural delivery services have

been initiated, substantial differences across a range of quality domains persist for rural

residents compared with urban and suburban counterparts (see Buettner-Schmidt, Miller,

& Maack, 2019). Ely et al., 2018). There has been little evaluation of the CCDs and PSRs

on hospital mergers and acquisitions for rural patients based on the Donabedian’s

conceptual framework of quality of care through the triad of structure, process, and

outcome (see Ayanian, & Howard, 2016; Pazargadi, Abedsaeedi, Majd, & Lankshear,

2008). The findings of this quantitative research study, therefore, fill the gap that exists in

the current literature and could offer an in-depth description process that can be used to

articulate a definite need for quality improvement initiatives to ensure the level of

readiness and adaptability necessary for quality patient safety, and provide a useful outlet

for minimizing CCDs (see Moore et al., 2015). Assessing the association of structure,

process, and outcome variables in RHMs will not only help to reduce PAEs, but it will

also provide decision-makers with system-level indicators of quality and assist them in

exploring strategies that can be used to improve organizational performance. In Section 2

of this study I presented the research design and the specific procedures used in

conducting this study.

Section 2: Research Design and Data Collection

Introduction

Since 2010, over 380 rural hospitals have merged, affecting access to quality of

care and triggering complex PSRs and associated CCDs (Williams Jr. et al., 2018). The

burden affects nearly 60 million people who reside in rural counties across the United

States including millions of Medicare and Medicaid beneficiaries (CDC, 2017).

Numerous studies have recognized the multiple hurdles that rural residents encounter

when accessing medical services, including a disintegrated healthcare delivery system,

stretched and diminishing rural health workforce, and unaffordable medical costs (AMA,

2018). The AHRQ (2018) and NC RHRPRC (2014) both acknowledged that hospital

mergers and acquisitions are modifying the face of health care in rural communities

across the United States.

With the significant expansion of hospital mergers or acquisitions, there is a

problem with the quality, efficiency, and patient safety in the U.S. rural healthcare

delivery, mainly related to RHMs (CDC, 2017; NC RHRPRC, 2014; Unger et al., 2018;).

Tens of millions of rural residents are impacted by such transactions each year (Hass et

al., 2018). Health care delivery in rural areas of the United States is mostly fragmented,

and because of its fractured nature combined with the health care needs of rural residents,

who may be at higher risk for multiple chronic conditions that require treatment from a

team of providers, coordinating care among specialists can be strenuous (see AHRQ,

2012; CDC, 2017; Davis et al., 2015). This can also result in unnecessary and costly

duplication of services as well as an increased risk of medical errors (see Berg, &

Grimeland, 2013). The disruption resulting from integrating two independent facilities

with opposite values is a system that often is costly and less effective at meeting the

needs of the rural patients, particularly in the immediate aftermath of the merger

(American Health Information Management Association, 2012)

Over 440,000 deaths were a result of PAEs in inpatient and outpatient U.S.

healthcare settings (James, 2013). Many of these PAEs were linked to the poor quality of

care. Rural U.S. citizens are 50% more likely to die from preventable causes, and

compared with urban populations, rural communities still experience a newborn death

rate 25% higher (Moy et al., 2017; Roth, Denney, Amiri, & Amram, 2019; U.S. NVSS,

2017). According to the U.S. Census Bureau (2016), unintentional PAEs such as drug

over-doses, increased chronic diseases and declined life expectancy, and the incidence of

diseases and morbidities are about 50% higher in rural areas than in urban cities. The

CDC (2017) and Harris et al. (2016) attributed this disparity to differences in

socioeconomics, health behaviors, and access to health services. These disparities have

even become more apparent when examining PSRs and CCDs due to hospital mergers

and acquisitions that have reached record-high spending.

Previous studies have shown some anticipated benefits of mergers, such as

improved financial performance, service consolidation, and operating efficiencies (Noles

et al., 2015). Various researchers have suggested mergers as an effective strategy for rural

hospitals in financial distress (Burkey et al., 2017). However, there has been a little

examination of the CCDs and PSRs that hospital mergers and acquisitions have on rural

patients. Declining reimbursement levels, increasing capital needs, a weak economy, and

easier access to credit have all attributed to a level of RHMs not seen in more than 2

decades (see Hass et al., 2018; Noles et al., 2015). To better understand the ramifications

of mergers and acquisitions for RHs, I developed the following four research questions to

guide this study:

RQ1: Is there an association of healthcare transformation changes in structures,

processes, organizational culture and values on clinical outcomes for rural

patients?

RQ2: What is the relationship between governance mechanisms and patient

outcomes in RHMs?

RQ3: How do best practices facilitate quality-focused initiatives and contribute to

eliminating unintended patient risks associated with structural changes and the

process of care?

RQ4: What is the relationship between clinician leadership and the quality of care

outcomes in RHMs?

To date, while many studies have focused on the association between hospital

consolidation and the financial aspects, no research has been conducted to explore or

identify specific areas where system complexities slow or inhibit the adaption of a

holistic patient safety culture in RHMs. Given the scale levels of need in rural

communities and despite the benefits of hospital mergers, patient care and safety must be

of great importance both during and after the transition planning phases (Noles et al.,

2015). To bridge this gap in the literature, I focused on four aspects of leveraging

organizational synergies in this study: change management, governance mechanisms,

system integration best practices, and the role of clinical leadership. Understanding the

relationship between hospital mortality and the potential performance comparisons of

rural hospitals post mergers could prompt policymakers to identify opportunities for

future quality improvement efforts that narrow identified gaps. The study results may

provide decision-makers with system-level indicators of quality and assist them in

exploring strategies that can be used to improve organizational performance.

The objective of section 2 was to present the research strategies and methods

related to data collection and analysis. In this section, I discussed the relevant theory and

the problem that has stimulated this study. My study was to identify an in-depth process

that can be used to articulate a definite need for a quality improvement initiative to ensure

the level of readiness and adaptability necessary for quality patient safety and provide a

useful outlet for minimizing CCDs. Network development leaders need to be aware of the

merging processes and best integration practices that will have positive influences on

reducing CCDs and PSRs. In this section, I provided the process of data collection

method from all relevant sources to find solutions to the research problem, test the

hypotheses and evaluate the outcomes. My aim was to present a thorough literature

analysis regarding the relationship between the following organizational synergy

complexities: governance mechanisms, hospital structures-care processes, clinical

leadership, and system integration best practices, and quality domains (i.e., CCDs and

PSRs) post mergers.

Research Design and Rationale

With the significant expansion of hospital mergers and acquisitions, there is a

problem with the quality, efficiency, and patient safety in the U.S. rural healthcare

delivery services, mainly related to RHMs (CDC, 2017; NC RHRRC, 2014; Unger et al.,

2018). Understanding the fundamental problem of quality of care in the disintegrating

healthcare system, which may result from integrating two independent facilities with

different values, is an essential first step to define the root cause of PAEs in RHs post

mergers. Such brokenness and unbalance due to systemic integrating misalignment,

competing interests, cultural differences, and inefficient allocation of resources, are at the

heart of the ineffectiveness of hospital processes, poor quality, and health inequalities. In

this study, I used a conceptual model to critically analyze gaps in healthcare quality in

RHMs, with the intent of systematically understanding the discrete components of quality

gaps and their potential mechanisms. Identifying specific areas of healthcare quality

problems in U.S. rural communities would provide actionable targets for future research

and quality improvement.

In this study, I applied a synthetical correlation conceptual approach that is

outlined by Donabedian’s vision for the evaluation of quality outcomes, which identifies

elements influencing care quality into structures, processes, and results, and furnishes a

theoretical perspective for research and interventions in quality improvement. This

approach was appropriate for developing and determining meaningful metrics to assess if

an association exists between organizational synergy complexities: governance

mechanisms, hospital structures-care processes, clinical leadership, and system

integration best practices and quality domains (i.e., CCDs and PSRs) post mergers. In this

study, I used the independent variable (i.e., RHMs) and the two dependent variables (i.e.,

CCDs and PSRs) to discern the relationship between rural communities and hospital

mortality through conducting a performance comparison of RHs post mergers.

Methodology -Theoretical Framework

In this study, I discussed some of the literature on the conceptual framework and

related metrics, either directly or potentially relevant to PSRs and CCDs in RHs post

mergers. This discussion was to provide a brief explanation of the selected potentially

useful framework and to demonstrate how the Donabedian’s quality improvement model

might be used to guide the development of this study. Thus, the purpose of this section 2

was to show that a theoretical perspective from this model could be applied to modify

structures and processes within healthcare delivery systems, such as integrating

organizations, to improve patient safety, information exchange, and minimize CCDs

(Ayanian et al., 2016).

In this study, I applied the Donabedian’s structure-process-outcome approach to

effect further improvement in hospital mergers and conduct a performance comparison of

RHs post mergers. The theoretical base for this study was the Donabedian’s quality

improvement model. Donabedian's three components approach for evaluating the quality

of care underpins the measurement of improvement initiatives. In this theory, Donabedian

(1988) explained that information about the quality of care could be drawn from three

categories-structure, process, and outcomes. Donabedian (2003) noted that each of the

three domains has advantages and disadvantages that necessitate researchers to draw

connections between them to create a chain of influence that is theoretically useful for

determining systems as well as designing experiments and interventions. This quality of

care model was developed to be blendable for care systems in disparate healthcare

environments and among various levels within a delivery system (Ayanian et al., 2016).

Donabedian further asserted that this theory could also be utilized to a sizeable healthcare

organization to evaluate the overall quality and align improvement programs across a care

system to improve quality and outcomes for a population.

Integrating healthcare organizations have inherent complex organizational

structures because of transformational changes in leadership, clinical care setting,

workforce challenges, changing patient expectations and demands, fiscal constraints,

increasing requirements for access to care, a mandate to improve patient-centered care,

and issues concerned with levels of quality and safety of health care (Daly, Jackson,

Mannix, Davidson, & Hutchinson, 2014). In addition to CCDs, health systems may deal

with complexity through various organizational structures such as decentralization,

centralization, or multiple channels of authority (Ayanian et al., 2016). Donabedian’s

conceptual model provided me with a practical framework for a research agenda that

could ultimately assess whether rural patients receive a lower quality of care and have

worse outcomes post mergers.

Because the theory describes that structure measures influence process measures,

which, in turn, affect outcome measures, the Donabedian’s quality improvement model

continues to serve as a touchstone framework in health services research in understanding

the actual effectiveness of new strategies or modifications within the care process (Moore

et al., 2015). Per the Donabedian (2005) model, it is critical for any improvement

initiative to have outcome measures, process measures, and structure measures, these

different types of actions have a unique objective in establishing whether the

improvement task has had the desired outcome. As the analysis of the methodologies of

this study, I focused on four dimensions of leveraging organizational synergies: change

management, governance mechanisms, system integration best practices, and the role of

clinical leadership to establish or evaluate the possible relationship between quality

domains.

Change Management Complexities

Various health care organizations, in the name of merging and consolidation, have

acquired a collection of overlapping practices that fail to achieve the intended goal of

safety system development (see Jennings et al., 2008). Previous researchers have

described the U.S. rural healthcare system as vulnerable, in part because of a confluence

of integrating health systems, each with seemingly competing interests, perspectives,

missions, values, and contrasting cultures (Noles et al., 2015). Among the evolving

hospital paradigm due to hospital mergers and a turbulent economy, the future for RHs

seems uncertain. As rural hospitals respond to increasing pressures of financial

constraints, they are opting to consolidate both diagonally and vertically to be able to

deliver integrated, cost-effective care; however, such shifts presents challenges for rural

hospitals, which often serve as the foundation for health care delivery in rural

communities and yet battle to conquer the ramifications of troubled economies, shortages

of health professionals, and public policy inequities (Moscovice & Stensland, 2002).

Unless merging organizations make a substantial long-term commitment to

changes in addressing the progressive deficiency that has been encountered by the

acquired health system, addressing the issue of quality of care, and managing

transformation can be challenging (see Baker, Denis, Pomey, & MacIntosh-Murray,

2010). Failure to cultivate an environment conducive to change and overcoming

resistance may lead to ever-increasing health disparities between rural and urban

communities (Cossman et al., 2017; Noles et al, 2015).

Governance Mechanism Capabilities

The future of organizational sustainment in hospital mergers requires highly

effective governance processes to confront a profusion of risks, including regulatory and

policy changes, performance, ethics, and quality of care issues. According to Baker et

al.2010, adopting a greater responsibility for quality and safety performance is

challenging for many boards and yet efforts to engage boards in improving care are based

on the rationale partnership to achieve economies of scale rather than patient-centered

care. Research suggests different cultures between integrating organizations will present

additional challenges with the balance of power resistance, goals misalignment, and an

array of clinical disruptions (Hass et al., 2018). However, overcoming such challenges

requires effective governance and heroic leadership, which is focused on generic based

quality and safety good practices. Donabedian conceptual model, therefore, has a

profound effect on helping hospitals to identify specific areas for clinical improvement

and created an environment that is conducive to improving the quality and safety of care.

System Integration Best Practices measures.

Healthcare is a sophisticated-adaptive system where interactions and relationships

of different components simultaneously transform and are shaped by nonefficient

integrated operations (Tsasis, Evans, & Owen, 2012). Intrinsically, it is critical for

performance initiatives to be integrated within the healthcare organization’s strategic

objectives post a merger. Key intentions, such as consolidating resources for efficiency,

expanding access points, boosting financial viability and market presence, focusing on

population health management, acquiring facilities for expansion, all require performance

improvement to be successful. Given the magnitude of barriers to implementing best

practices in RHMS, from cultural, clinical to financial, care institutions are confronted

with the challenge of identifying and creatively overcoming them to achieve and sustain

patient safety environments.

With confounding and inherent structure complexities emanating from nonbinding

long-term commitments to such an arrangement, participating hospitals are

understandably reluctant to exit from a service line (see Noether, & May, 2017).

Although implementing standardized tools such as decentralization, centralization, or

multiple channels of authority are not the quick fixes, the Donabedian’s quality

improvement model provides a vital framework of how change management complexities

may impact the adoption of a safety culture. Designing a quality safety culture model that

supports superior performance in terms of quality and safety as a result of effective

communication-best practices and standardized protocols in a manner that accomplishes

the hospital's needs is critical to significantly improving clinical, financial, and patient

outcomes.

Clinicians Leadership Roles

Research reveals that today’s leaders are hard confronted with multiple complex

issues that impact the ability to successfully implement cost-effective programs, maintain

efficient operations and services, and support patient safety initiatives (Daly et al., 2014).

Hass et al. (2018) argued that workforce challenges, changing consumer expectations and

demands, fiscal constraints, increasing requirements for access to care, a mandate to

improve patient-centered care, and issues concerned with levels of quality and safety of

health care all remain at the center stage of hospital mergers. With so many changes and

the sheer scale of competing priorities in RHMs, effective governance is imperative in

ensuring a high-quality health care system that consistently provides safe and effective

patient care.

In addition to deficiencies associated with condensed resources and demand in

rural hospitals, episodes of the prevalence of mortalities, cultures of poor care, and a

range of workplace difficulties have been associated with poor clinical leadership, and

these concerns have provided the impetus to evaluate clinical leadership roles more

carefully. Integrating hospitals and care systems in rural communities must adopt a

quality model that position structures, processes, and teams that support evidenced best

practices (Austin, Bentkover, & Chait, 2016). The pressure for managing these

responsibilities in striving for efficiency, control the costs and outcomes for an entire

episode of care, and sustainability is neither simple nor easy because it requires greater

adaptability in transformative change for the demands of a transforming healthcare

system (Kaufman et al., 2016).

In the changing environment where health systems need to audit their practices,

procedures, and facility cultures carefully during and after a merger to ensure that staff is

adequately prepared and trained for new challenges, the Donabedian’s quality model may

shed some light on the goals and responsibility for safety and quality which are usually

unclear. Consequently, recent research coupled with well-publicized patient safety

initiatives suggests that the Donabedian framework remains the dominant paradigm for

assessing and drawing inferences about the quality of care in a given system (Gardner,

Gardner, & O'Connell, 2013). Engaging clinicians during a merger and collaborating with

clinician leadership to drive best merging practices adoption post an alliance needs to be

thoroughly ingrained in hospital practices—to result in implementing a clear

accountability safety framework and relationships between all stakeholders.

Participants/Sample Size

Data Sources/Study Setting

Three hundred eighty hospital mergers during the period of 2010–2018 were

identified, and a random sample of pairs of rural hospitals that have merged or acquired

was drawn to establish a statistically efficient control cohort. Data used for the inquiry

were extracted from scholarly databases that track the mergers in RHs in U.S. citizens

from 2010 through 2018. RHs in U.S. rural counties nationwide during the period 2010 to

2018 was the focus of the study. The sources utilized consist of databases such as NC

RHRPRC publications, the health care services acquisition report produced by Irving

Levin Associates (ILAs), the Journal of Patient Safety, and the Journal of American

Medical Association, CDC and U.S. Census Bureau publications, Google Scholar and

Walden University’s library databases (see Neprash, Chernew, & McWilliams, 2017).

For triangulation in this correlation research, I used patient outcome measures of

mortality and readmission rate to study the effects of mergers on hospital quality of care

from the Hospital Compare website at Data. Medicare.gov.

Table 1.

Rural Hospital Mergers Ranked by State: Between 2010 and 2018

States

Mergers

States

Mergers

Oklahoma 36 Louisiana 7

Texas

Tennessee

Wisconsin

North Carolina

Oregon

Maine

Iowa

West Virginia

Pennsylvania 17 Kansas 3

Virginia

Georgia

South Carolina

Alabama

Illinois

Michigan

Ohio

Arkansas

Missouri

New York

Mississippi

Kentucky

Minnesota

Indiana

Florida

California

Connecticut

Maryland

Nebraska

South Dakota

North Dakota

Arizona

Montana

New Mexico

Washington

New Hampshir

Hawaii

Nevada

Idaho

Massachusetts

From “Rural Hospital Sustainability Index Data,” by Navigant, 2019

(http://www.navigant.com/navigant.com)

Table 2 shows that, on average, over 400 U.S. rural hospitals are vulnerable or are

at a 20% risk for closure or a merger. The risk of closure due to the financial crisis,

potentially hinder many U.S. rural citizens' access to care. Overall, data presented on this

table support that the risk for rural hospitals closures or mergers in U.S. states run from

lower 3.6% to 50% high (see Mosley & DeBehnke, 2019).

Table 2.

Number of Rural Hospitals that are At Risk to Merge/Close by State

States

Oklahoma 58 17 23.9

Total RHs

RHs at Risk

% at Risk

Texas 21 1 4.8

Tennessee 37 7 18.9

Wisconsin 95 17 17.9

North Carolina 15 4 26.6

Pennsylvania 41 9 22.0

Virginia 27 10 37.0

Georgia 22 1 4.5

South Carolina 63 26 41.3

Alabama 42 21 50.0

Illinois 75 9 12.0

Michigan 71 18 25.4

Ohio 65 7 10.8

Arkansas 49 18 36.7

Missouri 61 14 23.0

New York 48 9 18.8

Kentucky 65 16 24.6

Mississippi 64 31 48.8

Minnesota 89 19 23.1

Indiana 10 1 10.0

Florida 13 0 0.00

Oregon 28 1 3.6

Maine 40 8 40.0

Iowa 17 0 0.0

California 50 4 8.0

West Virginia 37 7 18.9

Kansas 101 29 28.7

Utah 127 12 9.4

Maryland 5 0 0.0

Nebraska 70 8 11.4

Washington 23 8 34.8

Idaho 40 6 15.0

Vermont 39 9 23.1

Wyoming 25 3 12.0

New Hampshire 17 5 29.4

Note. 19% of the U.S. population is served with rural hospitals at high operational risk.

Top risk States show a 20% or more and represent the vulnerability of communities.

Sampling and Sampling Procedures

Rural hospitals that have merged or acquired between 2010 and 2018 were the

target population for this research study. Three hundred and eighty mergers involving

rural hospitals from 2010 through 2018 were identified from the Health Care Services

Acquisition Report produced by ILAs. The Hospital Compare datasets, updated

periodically on the Medicare.gov Hospital Compare Website provided by the CMS, was

utilized to analyze and to pinpoint the impact of acquisition-driven transformational

changes on the quality performance of acquired rural hospitals. Based on the data

source’s capabilities in enabling researchers to compare the quality of care at over 4,000

Medicare-certified hospitals across the country, this data source was significant to the

study.

As highlighted in the previous section, this correlational study utilized archived

and secondary data from the Hospital Compare website at Data. Medicare.gov. The

literature review was comprised of journals and articles from the NC RHRPRC

publications, the Journal of Patient Safety, and the Journal of Medical Association, AHA,

CDC and U.S. Census Bureau publications, Google Scholar, and Walden University’s

library databases. Procedures for data collection was composed of transferring data in

Microsoft Excel tables, and data analysis was performed through IBM SPSS Statistics

Version 25.

Power Analysis

Power analysis using G*Power 3.1.9.4 was performed to ascertain whether the

number of hospitals incorporated in the study would be appropriate to detect a significant

correlation at a small effect size of (Z = 02). Considering the logistic regression with an

alpha set at 0.5, effect size of .15 with a 1.4 odds ratio and 80% predictive power, the

minimum number of hospitals included in the study was 348, as shown in Figure 1. In

this case, the predictive power of 80% was selected to determine the minimum lower

level of sample size. However, a final sample size of 380 rural hospitals that have merged

or acquired was analyzed to find if there is a compelling association that exists in the

population.

regression analysis priori function as represented in the graph

Data Collection, Strategy, and Management

Keywords such as patient risks, clinical care disruptions, organization structural

changes, organizational complexities, hospital mortalities, and rural hospital mergers

were utilized to assist in finding and locating relevant literature of this study. The library

sources searched include journals and articles from the NC RHRPRC publications, the

Journal of Patient Safety, and the Journal of Medical Association, AHA, CDC and U.S.

Census Bureau publications, Google Scholar, and Walden University’s library databases.

The literature search of my study explored to precisely assess the performance of rural

hospital alliances regarding structures, processes, and outcomes and evaluate the

correlation between quality domains. In this correlation research, I used patient outcome

measures of mortality and readmission rate to study the effects of mergers on hospital

quality of care.

This quantitative study exploited data from publicly available Medicare Mortality

Ratings Databases, Hospital Compare, and Hospital Evaluation Databases (2010–2018)

to research the performance differences. In addition to the analysis, data from scholarly

databases that track the mergers in rural hospitals in all 50 U.S. states within the period of

2010–2018 were extracted. These secondary datasets are available to the public, and the

data is de-identified with no personal identification to any patients. An authorization for

research to access the data was obtained from the Walden University Institutional Review

Board with an approval code of 07-18-19-0496865.

Instrumentation and Operationalization of Constructs

To better study the quality of care, level of clinical care disruptions, and

understand the mechanisms accounting for any potential performance differences in care

for rural patients post mergers, Donabedian (2005) conceptual model for Assessing

Quality of Care was exploited. This model was deployed to establish whether a structure

(organization structural changes in RHMs) and processes (hospital performance) are

associated with quality outcomes or may ultimately lead to increased PSRs (PAEs,

mortality, morbidity, rehospitalization rates, and clinical care disruptions). Other IOM

quality domains such as timely, effectiveness, equity, efficiency, safety, and

patientcenteredness care measures were considered to strengthen the model of the study

(AHRQ, 2016).

The statistical research tool that was appropriate to analyze data of this

quantitative study was IBM SPSS Statistics 25. Data collected from both the secondary

documents and from the Data.Medicare.gov was entered into SPSS computer software

and analyzed to answer research questions, test hypotheses, or disprove theories. As

outlined in the Donabedian’s quality improvement model that outcomes refer to the

effects of healthcare on the health status of patients and populations, I proposed that the

construct for this study to be model driven on patient safety culture.

Operationalization

This model strives to elucidate how specific organization structural complexities

inherent in hospital alliances and in providing care to rural patients can affect processes

and lead to poor outcomes. The study needed to display whether such relationships

between transformational changes and PSRs and CCDs post-RHMs does exist. According

to the Joint Commission’s Patient Safety Systems Chapter, patient safety culture is the

value of what a hospital is and how it does surpass its safety targets (Joint Commission,

2016). Evaluating and assessing the PSRs and CCDs of health care is critical because it

reflects how the institution is performing and leads to improved care post-mergers.

Emanuel et al. (2008) interpreted patient safety as a characteristic of health care systems

that reduce the incidence and prevalence of death and the impact of preventable events

and maximize recovery from such activities.

Because patient safety demands the design of systems in integrating organizations

to make risky interventions reliable and which cannot be directly detected, an attempt to

assess risk effect by operationalizing PSR variables (mortality rates, morbidity, hospital

readmissions, and PAEs) was made. Variables analyzed were identified and scaled in

numeric, and these include RHMs (independent variable) and PSRs and CCDs

(dependent variables). Understanding the relative strengths of the proposed model

components and tested association of measurement methods discussed here could help

investigators, clinicians, administrators, and policymakers meet this goal.

Table 3.

Dependent and Independent Variables

Dependent (PSRs and CCDs) Independent Covariates

Mortality rates

Hospital readmissions

Morbidity level

PAEs

Rural hospital

that have merged

or acquired

(RHMs)

Hospital

performance

merits (urban vs

rural hospitals)

Timeliness of

care

Safety of care

Patient

experience

Financial ratio

Data Analysis Plan

To summarize the collected information for interpretation, answer my four

research questions and presentation of the findings, the data collected from the secondary

documents were entered into SPSS computer software and analyzed as considered

appropriate. The specific variables investigated were RHMs (independent variables) and

patient risks and CCDs (dependent variables). Models constructed from hypotheses

concerning health systems and patient safety risks and clinical care turbulence pertinent

to the likelihood of a merger were tested using logistics regression analysis.

The technique allowed this study to determine the relationship between

organizational systems and the level of CCDs and the prevalence of PSRs after mergers.

The purpose of utilizing this analytical sampling technique was to determine if

statistically significant correlations exist between the two dependent variables (PSRs and

CCDs) and the independent variable (RHMs) while adjusting for each covariate of

organizational complexities (hospital care process, clinical care leadership, governance

mechanisms and system integration best practice indicators). Key findings and study

results will be presented in section 3. A total of four research questions were examined to

determine the association between organizational synergy complexities and CCDs and

PSRs in RHMs:

Research Questions and Hypotheses

RQ 1: Is there an association of healthcare transformation changes in structures,

processes, organizational culture, and values on clinical outcomes for rural

patients?

H01—There is no association of healthcare transformation changes in

structures, processes, organizational culture, and values on clinical

outcomes for rural patients.

Ha1—There is an association of healthcare transformation changes in

structures, processes, organizational culture, and values on clinical

outcomes for rural patients.

RQ2: What is the relationship between governance mechanisms and patient

outcomes in RHMs?

H02—There is no relationship between governance mechanisms and

patient outcomes in RHMs.

Ha2—There is a relationship between governance mechanisms and patient

outcomes in RHMs.

RQ3: How do best practices facilitate quality-focused initiatives and contribute to

eliminating unintended patient risks associated with structural changes and the

process of care?

H03—Best practices do not facilitate quality-focused initiatives and do not

contribute to eliminating unintended patient risks associated with

structural changes and the process of care.

Ha3—Best practices facilitate quality-focused initiatives and contribute to

eliminating unintended patient risks associated with structural changes and

the process of care.

RQ4: What is the relationship between clinician leadership and the quality of care

outcomes in RHMs?

H04— Clinical leadership in RHM and acquisition deals is not a

significant predictor of quality of care outcomes.

Ha4—Clinical leadership in rural RHM and acquisition deals has a

significant relationship with the quality of care outcomes.

Threats to Internal and External Validity

Challenges about secondary use of data mostly emerge around potential harm to

individual subjects and the issue of obtaining consent (Tripathy, 2013). However, in this

study, there were very few threats to the internal and external validity of data. As

previously highlighted in the data collection strategy section, the data contained in the

Hospital Compare at CMS.gov is a public-use secondary dataset that is patient

deidentified. In addition to secondary data analyses serving in this study as an economical

alternative to an expensive and time-consuming data collection process, most research

projects that consist entirely of secondary data analysis raise few ethical considerations

(Boo & Froelicher, 2013). There is the only area of concern to the reliability and validity

of datasets used in the secondary analysis that mostly emanate from the precision and

integrity of the techniques of data collection used in the initial data collection process.

Ethical Procedures

For ethical purposes, an authorization for research to access the data was obtained

from the Walden University Institutional Review Board with an approval code of 07-

1819-0496865. To maintain the security of the datasets, data for this study were

downloaded and stored in a password-protected archival file and deleted after the

investigation. To keep the threats of ethical issues at minimal, the Walden Institutional

Review Board supervised the collection of data and ensure that this research study

complies with all the university's ethical standards as well as U.S. federal regulations.

Summary and Positive Social Change Implications

Evaluating the quality of health care and promoting a culture of safety is crucial

because it reveals how the healthcare organization is performing and leads to improved

care. To better understand the impact of mergers and acquisitions for rural hospitals on

quality performance, this brief examined four research questions.

With hospital mergers on the rise, organizations that invest in building active

patient safety cultures stand to benefit. Understanding and properly implementing quality

initiative, is essential to a well-functioning health system and is necessary for any practice

interested in improving performance, patient safety, or clinical outcomes. In addition to

identifying opportunities for quality improvement. and foster a commitment to

excellence, a measure that carries substantial weight, as well as necessary implications

about safety of care, is a hospital's mortality rate. Through the study results, I hope to

support this commitment by helping health care leaders understand how reducing hospital

mortality rates and eliminating PAEs can improve health care safety and the quality of

care delivered to rural patients. While PSRs and CCDs in the U.S. rural hospital delivery

services may vary widely when calculated systematically and categorized according to

the level and type of care, patterns emerge that can highlight system defects. Although

several studies have documented the need for U.S. hospitals to reduce inpatient mortality

rates and PAEs, there is a scarcity of literature on effective methods to accomplish this

goal. Therefore, this research study adds to the knowledge needed for policymakers to

make decisions and establish a nationwide systematic process and develop a stronger

culture of continuous quality improvement in RHMs.

In Section 2, I documented and presented the quantitative processes necessary to

complete this correlational study. The conceptual framework of this study, study sample,

and sampling procedures, including instrumentation, operationalization of constructs, and

data collection strategy and management, were outlined and thoroughly discussed. In

addition to the restating of research questions, data analysis plan, and the purpose of

research design and how it could present meaningful results to the stakeholders of the

industry was proposed. This section concludes with highlighting threats to internal and

external validity, along with ethical concerns and procedures. My study results and

findings will be discussed and interpreted in Section 3.

Section 3: Presentation of the Results and Findings

Introduction

The principal goal of this study was to examine the potential association of

healthcare transformation changes in structures, processes, organizational culture, and

values on clinical outcomes for rural patients post mergers. A secondary intent of the

research was to enhance the understanding of the incidence of patient risks and clinical

turbulence in RHMs as a basis to reduce them. Periodic exposure of potentially PAEs in

rural communities might help public health departments identify emerging health

problems, monitor known issues, and focus interventions on reducing excess mortalities

in these areas (Moy et., 2017). In this correlational study, I focused on four aspects of

organizational synergies: governance mechanism capabilities, change management

complexities, clinical leadership, and system integration best practices. The governance

mechanisms (i.e., controls, policies, and guidelines), hospital structures i.e., (care

processes), clinical leadership (i.e., management cultures), and system integration best

practices were examined to determine their relationship to the prevalence of clinical care

turbulence and patient risks. Recent literature supports possible negative consequences

from mergers and acquisitions, although not conclusively (see Hass et al., 2018),

however, the potential health effects of mergers have received less attention in the

literature than other significant organizational changes, such as health care profitability

and cost cutbacks.

In this study, I employed the quantitative approach to describe PSRs and CCDs in

RHMs. In this research study, I accurately assessed and examined the performance of

rural hospital alliances regarding structures, processes, and outcomes and evaluated the

correlation between quality domains. I aspired to present an upgraded awareness of the

strategies harnessed by developmental leaders in integrating processes to safeguard rural

communities and promote a culture of safety excellence. In section 3, I discuss the data

collection methods, data selection criteria, data analysis methodologies, and a summary

of the analytical results.

The potential clinical consequences, PSRs, and outcome quality measures were

the dependent variables. Different structural factors and organizational synergies

consisting of new settings for providers, uncharacteristic system integration practices,

institutional relationships, governance mechanisms, and new patient populations served

as co-variables potentially associated with process and outcome measures. The RHMs

and the facility characteristics were the independent variables. A benchmark evaluation of

strategic controls, financial measures, and operating performances, as well as gaps and

regulatory requirements, were also included in the study as covariates because the

attributes are related to hospital quality performance competencies. (see Haas et al.,

2018). To further understand the areas of risks in RHMs, I utilized these structureprocess-

outcome independent and dependent variables to address the following research questions

and associated hypotheses.

RQ 1: Is there an association of healthcare transformation changes in structures,

processes, organizational culture, and values on clinical outcomes for rural

patients?

H01—There is no association of healthcare transformation changes in

structures, processes, organizational culture, and values on clinical

outcomes for rural patients.

Ha1—There is an association of healthcare transformation changes in

structures, processes, organizational culture, and values on clinical

outcomes for rural patients.

RQ2: What is the relationship between governance mechanisms and patient

outcomes in rural RHMs?

H02—There is no relationship between governance mechanisms and

patient outcomes in RHMs.

Ha2—There is a relationship between governance mechanisms and patient

outcomes in RHMs.

RQ3: How do best practices facilitate quality-focused initiatives and contribute to

eliminating unintended patient risks associated with structural changes and the

process of care?

H03—Best practices do not facilitate quality-focused initiatives and do not

contribute to eliminating unintended patient risks associated with

structural changes and the process of care.

Ha3—Best practices facilitate quality-focused initiatives and contribute to

eliminating unintended patient risks associated with structural changes and

the process of care.

RQ4: What is the relationship between clinician leadership and the quality of care

outcomes in RHMs?

H04— Clinical leadership in RHM and acquisition deals is not a

significant predictor of quality of care outcomes.

Ha4—Clinical leadership in RHM and acquisition deals has a significant

relationship with the quality of care outcomes.

In section 3, I discuss and present the core findings of this study as derived from

the methods applied in gathering and analyzing the research data. In this section, I

describe the data analysis and offer a summary of the answers to the research questions.

An overview of the time frame, response rates, discrepancies of the data set with baseline

descriptive logistic regression analysis, and demographic characteristics of the sample

were also provided.

Data Collection of Secondary Data Set

To examine differences in performances of rural hospitals that have gone through

mergers or acquisitions, I analyzed mortality data for U.S. rural residents from the NVSS

and readmission hospital data from Hospital Compare Datasets throughout 2010 to 2018

(see Mack, Jones, & Ballesteros, 2017). The NC RHPRRC center that performs

fundamental research in rural health care delivery matters and provides in-depth policy

analysis to address social issues affecting rural populations, defined and characterized

rural hospitals as facilities positioned in a non-metropolitan community (Health

Resources and Services Administration, 2018). Utilizing data from NC RHRPRC and

ILAs' Healthcare Mergers & Acquisitions reports collected from 2010 through 2018, I

identified a total of 380 rural hospitals that have merged or been acquired. Both NC

RHRPRC and ILAs maintain a publicly available map for tracking and counting RHMs

and closures. ILAs is the commanding developer of business literature in the senior

housing and care and healthcare mergers and acquisitions markets, and it is the most

extensive database capturing all publicly announced healthcare mergers dating back to

1993 with more than 26,000 transactions in 13 healthcare sectors (see Noles et al., 2015;

Su, 2017).

I systemically evaluated hospital quality using metrics published in the Hospital

Compare database collected by the CMS. Given the outcomes of care provided by health

systems are of the most significant concern in the transformation to the evidence-based

safety care model, I primarily focused on structure, process, and outcome measures of

quality. In this study, I used routinely collected CMS outcome measures to calculate

overall hospital quality. Inconsistent with the 2018 CMS star rating report, outcome

measures were grouped into six categories weighted by importance: mortality, safety of

care, readmissions, patient experience, effectiveness of care, and timeliness of care. To

reduce the variability in these measures of quality, I combined the six separate outcome

measures tracked by CMS into three composite outcome indices: one for mortality, one

for readmission, and one that combined both mortality and readmission measures.

Regression Analysis Methodology

I performed a logistic regression analysis to determine hospital performance and

safety level characteristics that were correlated with the likelihood of transformation

changes in structures, processes, organizational culture, and values in RHMs. A streak of

multiple regression models was then utilized to establish if there were statistically

significant variances in key hospital quality predictive indicators (i.e., PSRs and CCDs)

post-mergers as compared to non-merged RHs. In this study, hospital factors, such as

hospital governance effectiveness, critical hospital access status, best practices for

hospital quality, clinical leadership, and hospital care systems were included to adjust

systematic differences between RHMs and non-merged hospitals.

I categorized the performed regression analyses to evaluate the relationship

between hospital performance safety measures into six quality indicators- mortality,

safety of care, readmissions, patient experience, effectiveness of care, and timeliness of

care. In these regression analyses, hospital organizational characteristics, such as hospital

size, urban/rural location, ownership type, and teaching status were used as controls for

factors that could influence this association and provide some specifics on whether the

impact of acquisition might vary over time.

Mortality 20%

o Cancer mortality rate o Heart failure and Stroke -the cerebrovascular diseases

mortality rate o Mortality rates for pneumonia (PN), Chronic lower respiratory

diseases

Hospital Readmissions 20%

o All causes readmission rate o PN and Influenza readmission rates o Heart

failure (HF) and Stroke readmission rates o Hip/knee replacement readmission

rate

Effectiveness of Care 20%

o Preventive care (includes influenza vaccination measures)

oCataract surgery outcome o Blood clot

prevention o Pregnancy and delivery care

Safety of Care Outcomes (Hospital Associated Events). 20%

o Adverse Events-Infections o Diagnostic errors o Medication errors o Surgical

complications

Timeliness of care 10%

o Heart attack (HA) care o Emergency department care

oPreventive care (includes influenza

vaccination measures) o Cancer care o Use

of medical imaging

Patient Experience 10%

o Focused on quality, patent-centeredness, satisfaction, and other concepts o

Patients rating services according to the quality of care they receive

Figure 2. Quality-safety performance measures weighted by importance

Financial and operational performance measures weighted by importance

Total profit margin

Percentage of equity financing

Medicare inpatient services

Medicare outpatient revenue/total revenue

Study Results

In this analysis, using metrics published in the Hospital Compare database

collected by CMS, I evaluated hospital performance relative to four common conditions

and procedures, offering insight on the variation in clinical quality and outcomes across

the country. My principal analysis included 380 RHM transactions and 4,964 control

hospitals (for a total of 5,344 hospitals). The total sample of the study (shown in table 4)

consisted of 19,671,254 hospital readmissions at 5,344 urban and rural hospitals across

50 states in the United States. Of that total 7,445,133 were readmissions in urban

hospitals, which accounted for 30.0% of the unplanned hospital readmissions (UHRs);

5,705,756 were in nonmerged hospitals, which accounted for 23.0% of the UHRs; and

11,655,365 were RHMs, which accounted for 47.0% of the UHRs respectively. A total of

380 (7.1%) were in RHMs, 2,569 (48.1%) were urban hospitals, and 2,395 (44.8%) were

nonmerged facilities. The overall rate of cancer characteristics for RHMs was 3.9%, with

a 1.8% difference (95% CI, 1.59% to 1.65%) compared to urban and nonmerged hospitals

(p < 0.01). Both the rate gap for heart diseases and pneumonia in RHMs were higher

39.0%; and lower 2.4%, with a -0.6% difference (95% CI, -0.7% to -0.5%) and

(95% CI, -0.8% to -0.6%) in comparison to urban and nonmerged hospitals (p < 0.01).

The common deficient pattern for overall UHRs in RHMs in patient characteristics was

observed as Black readmissions at 15.3% with a -8.6% difference (95%, -8.16% to 6.9%)

compared to urban and nonmerged hospitals (p < 001). In comparison to the White

population, between RHMs and nonmerged and urban hospitals on UHRs, both Hispanic

and non-Hispanic (other) had a negative -0.4% difference (95%, -0.4% to 0.23%) and

0.27% difference (95%, -0.2% to -0.3%) respectively at (P < .00). Inconsistent with

previous studies, the findings of the current study support the argument that RHMs are

associated with modest deterioration of patient experiences, compromised quality of care,

and significant changes in readmission rates. As seen in Table 4, the prevalence rate of

chronic obstructive pulmonary disease (COPD) shows a 36.6% increase in urban

hospitals, 2.5% lower for nonmerged hospitals, and 38.1% for RHMs, which is 1.5%

higher. These statistics support that the rural U.S. citizens experience higher rates of

UHRs and mortalities from COPD than residents living in more urban areas.

Table 4.

Comparison of Patient and Comorbidity Characteristics of Hospital Readmissions by

RHMs 2010-2018.

Characteristics

Urban hospitals

n=2569

Non-merged hospitals

n=2395

RHMs n=380 Difference CI 95%

Hospital Admissions

7,445,133 (30%)

5,705,756 (23%)

11,655,365 (47%)

Health issues Cancer

5.8

4.2

3.9

1.8(1.59 to 1.65)

Heart diseases 38.4 38.5 39.0 -0.6 (-0.7 to-0.5)

Pneumonia

COPD

Patient demographics

Age, Mean

SD/Y

Black

White

Hispanic

Other

3.6

36.6

80.2 (8.6)

59.2

8.1

85.2

2.1

2.5

79.1 (8.4)

58.3

10.2

86.1

1.8

2.3

2.4

38.1

78.3 (8.3)

57.1

15.3

78.6

1.9

2.2

-8.6 (-0.8 to-0.6)

0.81 (0.8 to 0.9)

1.27(1.26 to 1.29)

3.08(3.0 to 3.3)

-8.16(-8.3 to -6.9)

0.02(-0.02 to 0.03)

-0.4 (-0.4 to 0.23)

-0.27 (-0.2 to -0.3)

Note. A positive confidence of interval (CI) at 95%-small or lower significant association

of CCDs and PSRs on RHMs; a negative CI at 95%-higher or elevated association of the

effects of RHMs. COPD represents chronic obstructive pulmonary disease and SD/Y

represents standard deviation age per year

Table 5 depicts the examined Hospitals’30-day readmission rates for patients who

had initially been evaluated for the following conditions: HF, PN, coronary artery bypass

graft surgery, hip and knee replacement or chronic obstructive pulmonary disease. The

analysis compared each hospital’s reported readmission rate to national averages for each

of the conditions to determine hospital performances. Six hospital performance measures

which are consistently collected by CMS over the period: three measures of 30-day

readmission rates for acute HA, HF, and PN and three measures of 30-day mortality rates

for HA, HF, and PN were included in the study. To minimize the volatility in these

measures of quality, I combined the six separate outcome measures tracked by CMS into

three composite outcome indices: one for mortality, one for readmission, and one that

combines both mortality and readmission measures. The CMS evaluates these trends in

measure results over time to monitor patterns, changes, and potential unintended

consequences in the measurement results.

Table 5.

Multiple Regression Analysis for Changes in Performance Measure Composite Post

Merger for Acquired Hospitals as Compared with Control Hospitals 2010-2018

Variable Unstandardized Coefficients Standardized Coefficients

B S. E. Beta T factor Sig

Performance

quality measure

(National

Comparison)

Safety of care -.174 .033 -.156 -5.345 .000

Patient

experience

.538

.055 .372 9.804 .000

Effectiveness of

care

.086

.028 .104 3.120 .002

Readmissions .523 .024 .609 21.771 .000

Mortality .051 .041 .041 1.253 .209

Timeliness of

care

-.024 .028 -025 -851 .395

CCD Composite 29.325 .00 .00 .00 .66

PSR Composite 44.974 .00 .13 .00 .16

Performance

period

. 0.3

2010 4.465 0.5 0.2 12.6 0.4

2011 3.838 -0.4 -0.6 15.9 0.5

2012 3.806 0.8 0.4 23.1 0.1

2013 3.779 1.0 0.1 31.7 0.8

2014 4.132 -0.8 0.3 18.3 -0.9

2015 4391 0.00 0.4 0.615 0.6

2016 4.506 0.3 0.615 15.5 0.12

2017 3784 1.1 0.11 21.3 -0.4

2018 3.762 1.7 18.4 0.2

National

comparison of

hospitalization

Mortality

Rural 166.6 0.1 0.1 0.1 0.9

Urban 195.3 0.3 0.3 0.2 0.4

Note. Sig= Significance p < 0.01, t-factor two-tailed; CI = confidence interval 0.95%,

CCD = Clinical care disruptions composite, PSRs composite = Patient safety risks Table

6 highlights the number of rated and unrated rural hospitals by CMS quality performance

classification. Based on the usefulness of the quality star rating for comparing hospital

quality and possible ways to reduce the PSRs and CCDS, the study assessed up to 90

quality measures that cover seven domains (mortality, safety of care, readmission, patient

experience, effectiveness of care, timeliness of care and efficient use of medical

imaging). Significantly, 1,300 total hospitals, which account for 73%, were not rated. Of

the 85, which received a 5-star rating, only 27.1% of the rural hospital met the CMS

qualifications compared to the urban hospitals which received a 72.9%. The data

suggested that, on average, rural hospitals with weaker quality performance and elevated

PSRs were negatively associated with the likelihood of the merger process.

Table 6.

Comparison of CMS Star Rated Category Percentage Ratio of Rural and Urban

Hospitals in Each Quality Performance Classification 2017

Total No.

Hospitals

Rural Urban CMS Star Rating

1,782

939

1,763

668

107

1,300 (73.0) 23

(27.1)

418 (44.9) 846

(48.0)

167 (25.0)

17 (16.0)

482 (27.0)

62 (72.9)

517 (55.1)

917 (52.0)

501 (75.0)

90 (84.0)

Not Rated (Missing)

5*****

4****

3***

2**

5,344 2,771 2,569

Note. From Hospital Compare Data archive, by CMS, 2017, U.S. Department of

Health

and Health Services, Baltimore,

MD.

(https://www.medicare.gov/hospitalcompare/Data/HCAHPS-Star-Ratings.html).

As seen in Table 7, the study analysis highlights the adjusted 30-day mortality

rates for each dependent variable stratified by hospital size. The more significant the

difference of each mortality rate among the sample of the hospitals, the higher the

chances of discovering statistical variabilities among the selected hospitals. Significant

variations were detected among RHMs, for-profit, and nonprofit hospitals. Among small

hospitals [ ≤ 99 Beds], the overall 30-day death rate was 9.9% of the total hospitalizations

for RHMs, 9.5% of the total hospitalizations for-profit hospitals, and 9.3% of the total

hospitalizations for for-profit hospitals with a 0.4% difference (95% CI, 0.1% to 0.7%).

For medium hospitals with bed count between 100 and 399, the overall 30-day death rate

was 8.6% of the total hospitalizations for RHMs, 9.3% of the total hospitalizations for

nonprofit hospitals, and 9.4% of the total hospitalizations for for-profit hospitals with a

0.8% difference (95% CI, 0.4% to 1.3%). Among the large hospitals with 400 bed count

or more, 9.3% was accounted for the overall 30-day death rate for RMHs, 8.9% was

accounted for the overall 3.-day death rate for nonprofit hospitals, and 9.1% was

accounted for profit hospitals with a 1.2% difference (95% CI, 0.9% to 1.5%) at p < 0.01,

and the outcome differences were statistically significant by RHMs for the overall

hospitalizations in this size category. Of the 380 RHMs compared to the 3,664 for-profit

and 1,300 nonprofit hospitals, there were modest differences in both surgical procedures

and all medical conditions.

Differences in the structure and process of care between RHMs (for-profit and

nonprofit) and urban (nonprofit and for-profit) hospitals may also be a cause for the

increased morbidity rates in RHMs. Typically, rural hospitals face unique challenges

compared to other, more extensive facilities, with most pressing issues of coordinating

and managing care. High volume patient turnover, workforce shortages, and an aging

infrastructure that institution stakeholders do not always have the resources to update.

Besides, rural hospitals are often geographically isolated, which further limits these

health systems easy access to resources. These unique complex challenges of care and

continually changing processes of care and culture may create conditions in which

complications, particularly infections, occur more frequently. Hence, the increased

mortality and hospitalization rates observed in certain circumstances and operations in

RHMs in this study might be due to the characteristics of patients, and not necessarily

because mergers compromised the processes in the rural hospitals

Table 7.

Correlation of Between Adjusted 30-Day Hospital Readmissions and Rural Health

System Status by Health System Type 2010-20182010-2018

Total

admissions

Rural hospital

mergers n=380

For profit

hospital

n=3,664

Nonprofit

hospitals

n=1,300

Difference

(95% CI)

P value

Small hospitals

(≤99 Beds)

Cases

2,543,802 20,596,115 (9.9) 261,879 (9.5) 2,281,923 (9.3) 0.4 (0.1 to 0.7) .01

Comorbidity 1,462 201 860,064 (11.9 143,325 (11.3) 458,818 (7.8) 0.5 (0.1 to 0.9) .01

Surgical cases 77,779 36,050 (7.8) 9,605 (3.5) 32,144 (3.8) 0.3 (-0.1 to 0.7) .018

Medium hospitals

(100-399 Beds)

Cases

11,802,681

461,010 (8.6)

4,083,475 (9.3)

7,258,196 (9.4)

0.8 (0.4 to 1.3)

.003

Comorbidity 5,986,284 201,184 (11.3) 2,015,603

(11.8)

3,769,497

(11.8)

05 (0.04 to 1.0) .012

Surgical cases 496,611 20,614 (3.6) 182,446 (4.0) 293,551 (4.2) 0.6 (0.2 to 0.9) .011

Large hospitals

(≥400 Beds)

Cases

7,105,341

3,122,215 (9.3)

2,869,375 (8.9)

1,113,751 (9.1)

1.2 (0.9 to1.5)

.001

Comorbidity 3,171,715 1,276,866 (11.0) 1,348,310

(11.6)

546,539 (12.0) 1.0 (0.6 to1.4) .001

Surgical cases 334,814 144,976 (3.2) 140,154 (3.6) 49,684 (3.8) 0.7 (0.4 to 0.9) .001

Note. Standard errors. p < 0.01, p < 0.05, p < 0.1. A positive p-value indicates

statistically moderate/strong quality level association and a negative p-value signifies

weak association on performance quality level.

A Pearson regression (PC) coefficient analysis was conducted to determine the

correlation strength and direction between the continuous variables and the dependent

variable from the study sample (n = 5,344). Table 8 depicts the results of the PC

indicating a significant moderate positive relationship between all 30-day mortality

categorical and organizational synergies complexity characteristics. Hospital governance

effect (HGE) ratio (r = -733, p = .005); critical hospital access status (CHAS) ratio (r

= .238, p = .000); hospital integration best practice (HBP) ratio (r =.476, p = 0.01);

clinical leadership effect (CL) ratio (r = .430, p =.570), structural hospital care systems

(SHCS) ratio (r = .029, p = .0.01).The statistical analysis portrays the correlation of

hospital performance and safety level characteristics with the likelihood of transformation

changes in structures, processes, organizational culture, and values in RHMs.

Table 8.

Pearson Regression Coefficients of Organizational Synergies Complexity Between All

Categorical 30-Mortality Rate and Continuous Variables on RHMs

Source No. of Hospitals

(n)

R P

HGE (Hospital

Governance

Effect)

5,344

-.733

.005

CHAS (Critical

Hospital Access

Status)

5,344

.238

.130

HBP (Hospital

Integration Best

Practices)

5,344

.476

0.02

CL Ratio (Clinical

Leadership Effect

SHCS (Structural

Hospital Care

Systems)

5,344

.430

.029

.570

.0.01

Note. n=Total number of hospitals; r= Pearson correlation coefficient; Sig= Significance

p < 0.01, t-factor two-tailed.

In the analysis and based on the significance of the overall regression,

organizational synergies complexities (i.e., change management, governance

mechanisms, system integration best practices, and the clinical leadership) were assessed

to determine its impact on CCDs and PSRS due to RHMs. Statistical analysis, as shown

in Table 9, indicated that the effects of organizational complexities were significantly

predictive of changes in hospital performance (p < .001). The findings indicated that

much of the increase in the likelihood of transformation changes in structures, processes,

organizational culture, and values corresponded with the effect ratio of organizational

complexities. These results implicate not only patient care but ratings to rural hospitals,

which may be subject to the CMS unplanned readmission penalty for higher-

thanexpected 30-day readmission rates. Further, the findings from the study suggest that

hospital mergers may be related to impaired quality of care delivered at acquired

facilities, which fail to use performance indicators to support internal quality

management.

Table 9.

Logistic Regression Analysis of Organizational Synergies Complexity on Effectiveness

Hospital Integration (Merger Process)

Unstandardized

Coefficients

Standardized

Coefficients

B S.E. Beta t Sig

HGE ratio

-.029

.425

-.069

.945

HBP ratio .549 .034 .367 16.210 .000

SHCS ratio .373 .020 .089 18.460 .000

CHAS ratio .081 .024 .089 3.329 .001

CL ratio .024 -.079 .435 4.365 .002

Note. Dependent variables (mortality national comparison and hospital performance) were used in

the analysis.

Table 10 presents the results of the means for the calculated clinical quality and

safety scores, the predicted possible scores per CMS, the gaps, and the percentage gaps.

On average, 380 RHMs included in the study sample had a greater than 16.9% gap on

most of seven hospital performance and safety measures. Safety composite measure

highlights the highest average percentage gap of 42.65 on catheter-associated urinary

tract infection (CAUTI), followed by clinical care domain-PN 30-day mortality rate with

24.35%. Although, other the performance measure variables show lower percentage gaps

in both the clinical care domain (CCD) and safety domain scores (SDS), the predictors

were statistically significantly correlated to at least one organizational synergies

complexity (i.e., change management, governance mechanisms, system integration best

practices, and the clinical leadership) due to effects of RHMs.

Table 10.

Clinical Care Domain Estimates and Quality Gaps in Deaths by 30-Day Mortality and

Hospital Unplanned Readmissions Rate in RHMs.

Calculated score

identified (X*)

Mean S. E

Predicted score

(X**)

Mean S. E

Score difference

(X*-X**)

Mean S. E

Score gap %

Mean S. E

Clinical care

domain (CCD)

Acute myocardial

infarction (AMI)

30day mortality rate

11.09 1.65

10.64 0.56

0.45 1.09

4.06 13.74

Heart failure (HF)

30-day mortality

rate

11.56 1.96

9.78 0.61

1.78 1.35

15.40 14.76

Pneumonia (PN)

30day mortality rate

Safety domain

scores (SDS)

Surgical site

infection (SSI)

Catheter-associated

urinary tract

infection (CAUTI)

30 -Day Unplanned

Readmissions (PN)

30-Day Unplanned

Readmissions (HF)

1.15

0.75

0.68

25.09

19.35

0.28

0.08

0.15

2.39

1.83

0.87 0.03

0.65 0.03

0.39 0.06

23.93 1.05

16.68 0.69

0.28

0.1

0.4

1.16

2.67

0.25

0.05

0.09

1.34

1.14

24.35

13.33

42.65

4.62

13.80

25.08

15.35

11.65

5.82

6.73

Note. (X*-X**)/X*100= Gap quality percentage, X* is Calculated score and X** is

Predicted score and S.E. represents standard error percentage

Statistical Analysis

Four research questions were staged to address the problem that had not been

thoroughly reviewed in the past decade on RHMs literature. Each issue was assumed to

support previous studies’ proposition that the problem is more research is needed to

outline the neglected relationships of RHMs on rural communities. As presented in

Section 2, these four research questions serve as the basis for this study’s research,

design, and analytic, and are as follows:

RQ 1: Is there an association of healthcare transformation changes in structures,

processes, organizational culture, and values on clinical outcomes for rural

patients?

A Pearson regression model resulted in the null hypothesis being rejected. It

indicated that much of the increase in the likelihood of transformation changes

corresponded with the effect ratio of organizational complexities. This study suggests that

RHMs may be associated with impaired quality of care, clinical care turbulence, and

PSRs escalation. The model was statistically significant at p < 0.01, which means the

results suggested the alternative hypothesis (Ha1) failed to be rejected, and there was no

support for the null hypothesis (H01).

RQ2: What is the relationship between governance mechanisms and patient

outcomes in rural hospital mergers?

Hospital governance effect ratio displayed a weak association matrix result of (r =

-.733; p < .005). The model revealed hospital governance effect ratio had a weak

correlation between all 30-day mortality categorical and organizational synergies

complexity characteristics, and yet significant predictor to PSRs and CCDs in RHMs.

However, the model was statistically significant (p < .005), and the results demonstrated

that there was no enough support not to reject the alternative hypothesis (Ha2). RQ3:

How do best practices facilitate quality-focused initiatives and contribute to eliminating

unintended patient risks associated with structural changes and the process of care?

Hospital integration best practices was a relevant predictor of quality-focused

initiatives in eliminating unintended PSRs and CCDs in RHMs. Hospital integration best

practices effect ratio represented a correlation matrix results of (r = .367; p < .000). In

this model, results suggested the alternative hypothesis (Ha3) failed to be rejected, and

there was no support for the null hypothesis (H03).

RQ4: What is the relationship between clinician leadership and the quality of care

outcomes in RHMs?

Clinical leadership was a significant predictor of the quality of care in RHMs.

Clinical leadership ratio displayed a correlation matrix results of (r = .435; p < .002). At

p < .002, the study results revealed an alternative hypothesis (Ha4) failed to be rejected,

and there was no substantial support for the null hypothesis (H04).

The association between total hospitalizations, mortality rates, and currently

RHMs (nonprofit and for-profit hospitals) had a positive effect relationship and in both

surgical procedures and all medical conditions were statistically significant (p < .01) by

RHMs for the overall hospitalizations in this size category. In addition to substantial

variations in hospital performance profiles which were detected among 380 RHMs

(forprofit, and nonprofit hospitals) with a mean annual hospitalization on medical

conditions at 95% confidence level, had higher than expected mortality rates for all

metrics of 27.8% in total, which had a higher odds of poor hospital performance than

nonmerged hospitals.

Summary

Unsafe healthcare has always remained one of the challenges of the U.S.

healthcare delivery system and a well-recognized health issue, usually listed among the

top three causes of deaths in the United States with rural residents experiencing more

significant hardship (NC RHRPRC, 2014). Declines in hospital performance in the

quality of care have been associated with more frequent mortality and hospital

readmission rates. Several mergers are consummated to capitalize on new geographic or

demographic markets, expand product offerings, facilitate the acquisition of critical

employees, boost productivity, reduce competition by absorbing a rival company, or even

more long-term strategies (Botje et al., 2016). Amid the need to maintain such a

competitive advantage, healthcare administrators must perform the intricate task of

keeping pace with the robust healthcare environment – continually changing patient

volumes, increasing supply costs and quality requirements, personnel shortages, and

utilization standards. Regardless of the motive, the process and outcome must be

measured to assess, identify, and eliminate the root causes of poor performance during

and after the transition.

While the findings of the study revealed modest differences in the overall risk of

hospital readmissions and 30-day mortality rates among rural versus urban hospitals, it

did find that the location of the hospital and its acquired characteristics have a significant

effect on performance and patient safety levels. The study suggests that patients

discharged from hospitals in large RHMs had a 29.3% higher risk of unplanned

readmissions as compared to those released from for-profit and nonprofit urban hospitals.

Concurrently, the study found that patients treated at merged rural hospitals have an

average 8.5% higher risk of dying or experiencing one or more complications during a

hospital stay than if they were treated at nonmerged hospitals in that procedure or

condition. These findings further confirm the need for critical evaluation of commonly

known arguments that integrating health systems improves efficiency, access to care, and

quality of care, and may lower costs. Although integrating health systems may result in

financial and operational efficiencies, declines in health outcomes should be avoided. My

findings present an opportunity to identify systemic reasons for the higher PSRs and

CCDs observed in common medical conditions and after certain operations in RHMs.

The statistical analysis results revealed that all four null hypotheses could be rejected,

indicating that organizational synergies complexities represented by logistical differences

among RHMs all showed the trajectory persisted pattern of CCDs and PSRs. Section 4

will discuss and present a detailed synthesis of my study’s application to professional

practice, gaps in research, limitations and implications for social change. This section will

also list recommendations of the study and suggestions aimed at improving future

research in light of these study findings.

Section 4: Application to Professional Practice and Implications for Social Change

Introduction

As healthcare systems deal with the complex regulatory demands of the quality

improvement and quality control of healthcare, as well as the challenges of a shrinking

economy, there is always the possibility that one facility will merge with another.

Maintaining a hospital or health system is costly and combining health systems can help

counterbalance some of the financial burdens; however, those mergers may trigger more

than just performance dilemma. In the long term, hospital affiliations might cause an

upheaval of clinical disruptions and impact quality of patient care (see Hass et al., 2018)

Because RHMs often bring about changes in patient populations, activities adjustments,

and facility infrastructure, safety risks may increase.(see Jennings et al., 2008; Johar et

al., 2014). Researchers have identified these most pressing patient safety concerns that

may occur during mergers or system expansions to play a significant role in high

mortality rates and poor patient outcomes (Hass et al., 2018; Noles et al., 2015). The

burden affects nearly 60 million people who reside in rural counties across the United

States, resulting in over 440,000 deaths yearly, and many of these PAEs are avoidable

using system-oriented shared safety culture (CDC, 2017).

Adjustments of therapeutic activities proposed by leadership, down-sizing,

redistributions of staff, conflicting cultures, location changes, new settings for providers

and hospital structure fragments may all have unforeseen effects (Berg & Grimeland,

2013). Even though hospital mergers typically have several positive goals, arguments in

favor of a merger must be investigated carefully for idealistic oversight of the prevalence

of PSRs and CCDs of RHMs. In many cases, teams with narrow proficiency in patient

safety are typically responsible for implementing healthcare mergers, acquisitions, and

affiliations (AHRQ, 2018). Considering these issues, the primary objective of this study

was to examine the potential association of healthcare transformation changes in

structures, processes, organizational culture, and values on clinical outcomes for rural

patients post mergers. The focused intent of the study was to enhance the understanding

of the incidence of patient risks and clinical turbulence in RHMS as a basis to reduce

them.

In this quantitative analysis, I focused on hospital performances and quality

outcomes on RHMs and acquisitions that occurred between 2010 and 2018. Regression

analyses were conducted to examine the association between hospital performance safety

metrics that were categorized into the following six quality indicators: mortality, safety of

care, readmissions, patient experience, effectiveness of care, and timeliness of care. I

used hospital organizational characteristics, such as hospital size, urban/rural location,

ownership type, and teaching status as controls for factors that could influence this

association and provide some specifics on whether the impact of acquisitions might vary

over time. Publicly available data used in the study were from Medicare Mortality

Ratings Databases, the U.S. NVSS archives, Hospital Compare, Hospital Evaluation

Databases and hospital quality indicators from the CMS.

Interpretation of Findings

I proposed a theoretical lens for understanding and evaluating complexity in

integrating healthcare systems based on degrees of uncertainty and the involuntary nature

of many change processes and cultural clashes (see Resta, Sonnessa, & Tànfani, 2017).

Considerable concerns have been highlighted for why transformational changes, like

mergers and acquisitions, often lead to adverse health effects and increased CCDs.

Undoubtedly, systematic problems in clinical care turbulence are at the root of most

detrimental events that arise post mergers. Amid such complex mechanical and

organizational changes, the upheaval accompanying restructuring efforts through mergers

can also be related to lower job satisfaction among clinicians and increased burnout.

Furthermore, increased conflict fueled by differences between merging entities relating to

unique operation systems, mission, and values, often makes clinicians feel more uncertain

about their responsibilities, what is expected of them, and how the changes will affect

their patients.

The findings of this study highlight that RHMs have a significant effect on

increased mortality and declined health status in U.S. rural communities, showing a

catastrophic inequality burden emerging as a significant determinant of the health of

populations. The results indicate that mergers have a particularly significant effect on

PSRs during and after the consolidation taking place. Previous studies have reported that

healthcare mergers result in a more extensive health system offering financial incentives

to smaller hospitals, including upgrades to supplies and equipment; protocols, and

information technology, such as electronic patient records and security systems (Hass et

al., 2018). However, in the present study, I found that these changes in infrastructure

often create challenges for clinicians, and without planning, such changes can cause

significant patient risk. Because of the seriousness of long-term, disruptive work

environment and the high prevalence of clinical risks, RHMs have a substantial effect on

socioeconomic burdens, prolonged delays in treatment, and patients often experience

travel and distance barriers to get care. Managing the process of a merger better should

lead to a more cautious approach to the likely gains, provide an understanding of the

problems that are likely to arise in the period of change, and anticipate and avoid harmful

consequences.

Contrary to the findings of the Charles River Associates Report that mergers have

become one of the critical means through which hospitals can provide their communities

with high-quality, convenient, and cost-effective care by allowing hospitals to create

connected networks of care (see Noether, May, & Stearns, 2017). This study revealed the

primary impetus is often financial rather than clinical. RMHs can have substantial effects

on clinical care and patient safety, particularly when clinicians encounter changes in their

practice setting, patient population, or infrastructure. Routine tracking of potentially

excess mortality and admission rates from the various causes of risk in rural and urban

areas might assist public health officials in monitoring substantial rural health disparities

and selecting effective programs and policies to improve the health of residents of rural

areas. With the increasing trend of RHMs not slowing down, rural patients will require

these data to compare the service they receive from merging different hospitals. Such

findings should, therefore, be increasingly made more accessible to the public.

Limitations of the Study

I identified a few limitations concerning this quantitative study. The principal

limitation of this study is that data collected on Hospital Compare comes from hospitals

that discretionarily offer their data for public reporting, so it is possible that there was a

systematic scoring bias in hospitals’ performance reporting across all measures (see

AHRQ, 2016). Although this data set contains hospital-specific performance on the

process of care quality measures and patient experience of care measures for over 4,500

hospitals, such a data set is vulnerable to the incompleteness of reporting. Its vulnerability

lies in the fact that hospitals can designate to submit data for all the quality indicators or

can elect to withhold the data from display on Hospital Compare if specific core measure

scores were particularly weak. However, CMS star rating reports have been used broadly

for hospital quality benchmarking, including analyses of the diagnoses used in my study,

and such benchmarking is valid compared with clinical data and large administrative data

sets. To include as many hospitals in my analyses and to reduce the variability in these

measures of quality, I combined the six separate outcome measures tracked by CMS into

three composite outcome indices: one for mortality, one for readmission, and one that

combined both mortality and readmission measures.

Secondly, the precision with which hospital performance can be ranked is

impacted by variations in hospital capacity, the class of subjects they care for, and

sampling strategies, (i.e., the numbers of patients used to calculate reported performance

rates from the quality measures). The only process of care measures applied to compute

rankings consists of one comprehensive prevention measure and mechanism of care

measures from six clinical topic areas: mortality, safety of care, readmissions,

patientcentered score, effectiveness of care, and timeliness of care. Even within these

specific clinical areas, they reflect only a portion of the hospital performance score

rankings.

However, despite limitations (i.e., lack of clinical detail, susceptibility to patient bias and

health literacy levels., time lags, and variations reporting systems), quality improvement

experts view public data as a possible and valuable source to evaluate hospital quality

performance.

To neutralize the severity of these data constraints, I used other data sources that

can provide information about healthcare quality in this study. Publicly available

Medicare Mortality Ratings Databases, Walden University Library databases, and

Hospital Evaluation Databases were used to assess whether the toll of specific mortality

diseases is worsening or decreasing, and to understand where best to target medical

interventions so they can play a critical role. Recognizing these limitations, I believe the

merits of the public data set outweigh many of these concerns and that it enabled a robust,

timely analysis of clinical outcomes with a validated data source and was highly relevant

to assessing the effects of the RHMs (see Loehrer, Chang, & Scott, 2018). Specifically,

ratings, such as Hospital Compare and CMS quality reporting measures, as well as other

sources included in the study, such as AHA reports, strengthen accountability and offer

differing arrays of performance measures that may induce hospitals to improve their

quality of care.

Gaps in Research

The findings of this quantitative study have shown the need for better planning,

and critical consideration in many areas of measure of success that can identify and verify

practices that improve safety in RHMS and that before strategic hospital merging

arrangements can be pursued, significant gaps in knowledge must be filled. Clear

recognition is needed in all three of the domains of patient safety areas identified: poor

outcomes of care due to PAEs, structural enablers to unsafe care, and disruptive processes

of care that lead to safety risks. In this era of closer scrutiny of cost and quality of care,

the field of hospital consolidation lacks actionable and outcome-oriented measures in this

area.

In this respect, understanding the true needs of the affected rural patients

generates an impetus for much fundamental work with integrating health systems and

their representatives in order to transform the present situation. However, lack of

methodological uniformity in the identification and measurement of PAEs, including

mismatch cultures and weak evidence-based best practices related to quality

management, continues to delay the development of knowledge about which

interventions should be adopted and how to encourage adoption of patient safety

practices.

Recent studies have examined the effects of changes in the health care market

segment on healthcare organization performance due to hospital merger expansions.

These studies have confirmed that the pursuit of economies of scale and coordinated care

initiatives have led to the escalation of price competition among hospitals and that price

competition has resulted in lower rates of cost growth, lower prices and price-cost

margins, and changes in the adoption and use of technology (Haas et al., 2018; Loehrer,

Chang, & Scott, 2018; Noles et al., 2015). However, the effects of changes in health

structure on the quality of care provided by hospitals after a merger are less well

understood, and this complex changing environment lacks functional and

outcomeoriented measures in this area. In this study, I offer tactical recommendations for

future measure performance and endorsement to close this vital gap. The study

recommendations can be instrumental in organizing measure development efforts to

ensure that clinical, financial, and human resources are strategically targeted during the

transition and post merger phase.

Recommendations

As found in this study, the U.S. hospitals have been merging at a rapid pace for

more than 2 decades, forming influential organizations that have been creating countless

long-term, challenges for clinicians and significant PSRs. The current understanding of

the extent of CCDs and PSRs in RHMs and in their causes are underestimated. This is

especially true for rural hospitals and those with integrating systems in transition, where

most of the U.S. rural population lives and receives health care from. There are even

substantial gaps in our knowledge about how to minimize the PSRs and address clinical

turbulence in RHMs, often brought about by changes in patient populations, activities

adjustments, staff distributions, and facility infrastructure.

Although some strategies have been developed, the U.S. health services know

little about how to address many of the problems identified. Adopting and implementing

patient-centered strategies to improve the patient safety culture model will be the first

step. Presently, potential or actual adverse events (e.g., mortality rates and hospital

readmissions) are substantially underreported, and healthcare organizations will require

the identification of specific areas where system complexities slow or inhibit progress and

the development of solutions geared toward overcoming impediments and failures (IOM,

2011). Achieving this objective will require that healthcare administrators and

developmental business leaders become aware of patient-centric and embrace valuebased

healthcare practices when entering consolidations.

Aiming at the efficiency build up and focusing on strengthening capacity for

patient safety initiatives, various steps must be taken to foster a complete successful

patient safety culture model in RHMs. A few of the most critical areas to consider when

constructing an operational efficiency for merging health systems include a clear

leadership structure, system-oriented shared safety culture, commitment to the

standardization of clinical best practices, and well-defined ground-level integration

strategy. Given the many variables and catalysts influencing the systematic change in

U.S. rural hospital delivery services, careful planning, thorough due diligence, and

strategic integration post transaction can simplify the process to overcome poor patient

outcomes stem from systemic failures in the delivery of care.

Previous research suggests that organizational culture affects the success of

postmerger integration and clinical collaboration (Ovseiko, Melham, Fowler, & Buchan,

2015). A further increasing body of literature advocates that rural hospitals often lack the

necessary means to adopt patient safety strategies commonly found in more extensive

facilities due to organizational, technological, staffing, and financial constraints (Coburn,

& Gage-Croll, 2011). This research, however, aims to provide a framework for attaining

the full benefits of a merger — operational efficiencies, economies of scale,

organizational performance and enhanced clinical value.

Research advocates that many mergers or consolidations do not go according to

plan or fail to achieve the expected outcomes, and culture is often the driver of such

failure. Although there are innumerable factors that can lead to merger and acquisition

failure, cultural mismatch is one of the mentioned reasons. To overcome the risk of

failure, the integrating health systems must focus on understanding and developing the

new entity safety culture. While overcoming barriers of learning may take several years,

experts in merger and acquisition deals suggest that leaders from the acquiring facility

should consistently and frequently articulate the mission and goals of the transaction.

From the expansive view of mission statements to the fine details of daily schedules,

communications should focus on supporting integration efforts and building the

foundation for a typical patient safety culture between the organizations. By incorporating

this approach, developmental leaders could help ensure that RHMs go according to plan

and achieve their desired results.

Further research is recommended to help healthcare organizations understand and

quantify patient safety events and areas of vulnerability in their institutions post mergers.

It is feasible that gaining a better understanding of the systemic factors that combine in

unanticipated ways and threaten patient safety may also help to shape a successful

hospital alliance. While the study outlined a measure of success in identifying and

verifying practices that improve safety, more work is required in authenticating how best

practices get executed and amalgamated into clinical processes, and in corroborating,

there is a full and continual commitment to providing the safest care possible. Finally, I

recommend this study be expanded to hospital mergers in U.S. metropolitan areas to

ensure this commitment does exist throughout the continuum of care. In addition to

evaluating hospital performance differences post mergers, future investigators will have

the potential to provide more insight regarding the motive behind mergers and acquisition

transactions among rural hospitals.

Implications for Professional Practice and Social Change

Professional Practice

As health systems struggle amidst regulatory changes, short cut in Medicare and

Medicaid payments and shrinking profits, a merger can seem like precisely the cure-all

needed to ensure survivability. Owing to this preposition, rural hospitals are attractive

targets, and the trend of RHMs is likely to continue. Given the critical need for health

services and obstacles to access to care of rural citizens in the United States faces, it is

particularly important to understand any impact to the community caused by these

monumental transactions. Despite the benefits of the mergers and acquisitions, patient

care and safety must be prioritized both during and after the transition. Studies of this

kind are essential to set a foundation of a proactive, purposeful and sustained patient

safety model which includes integrating cultures into the organization and deploying

best-practice merger and acquisition project management techniques and aligning

executive and functional leadership.

Policymakers and developmental business executives must understand that

integrating two or more organizations into one cohesive operating unit takes intense

planning and a focus on post merger logistics. Inherent in that melding necessary steps

are cultural assimilation, process communication, technology, and system integration.

Understanding these tasks and being aware of potential correlations that exist potential in

healthcare transformation changes in structures, processes, organizational culture, and

values on PSRs and CCDs for rural patients after mergers, could facilitate healthcare

executives to identify high-level strategies on quality improvement. By studying quality

indicators carefully in RHMs and raising institution awareness of efforts to reduce

inpatient mortality and PAEs in underserved communities, I hope to have provided a

holistic view into long-term implications and other operational community factors.

Positive Social Change

With hospital mergers on the rise, organizations that invest in building active

patient safety cultures stand to benefit. Understanding and properly implementing quality

improvement initiative is essential to a well-functioning health system and is necessary

for any practice interested in improving performance, patient safety, or clinical outcomes.

In addition to identifying opportunities for quality improvement, foster a commitment to

excellence, a measure that carries significant emotional weight, as well as essential

implications about quality of care, is a hospital's mortality rate. Through this study

results, I hope to support this commitment by helping health care leaders understand how

reducing hospital mortality rates and eliminating PAEs can improve health care safety

and the quality of care delivered to rural patients. While PSRs and CCDs in the U.S. rural

hospital delivery services may vary widely when calculated systematically and

categorized according to the level and type of care, patterns emerge that can highlight

system defects. Although several studies have documented the need for U.S. Hospitals to

reduce inpatient mortality rates and PAEs, there is a scarcity of literature on effective

methods to accomplish this goal. Therefore, this research study adds to the knowledge

needed for policymakers to make decisions and establish a nationwide systematic process

and develop a stronger culture of continuous quality improvement in RHMs.

Conclusion

Several recent articles cited RHMs may affect the delivery of quality of care and

shrink the availability of services as hospitals work toward greater efficiency in cost

control (National Rural Health Association, 2015). When financial performance becomes

the goal, fragmentation of productive tasks, down-sizing, redistributions of staff,

conflicting cultures, location changes, new settings for providers, and hospital structure

fragments may all have unforeseen effects. All these factors affect a community and play

a significant role in the prevalence of mortality, hospital readmission rates, and complex

clinical disruptions. Whether the primary driver of a merger is the pursuit of economies

of scale, the ability to decrease unit costs or improve productivity and outcomes through

increased volumes, there is a problem with the quality, efficiency, and patient safety in

the U.S. rural healthcare delivery, mainly related to RHMs (CDC, 2017; NC RHRPRC,

2014; Unger et al., 2018).

The examination of processes provides the critical information that can be acted

upon, establish strong relations between structures and outcomes, and promote strategic

collaborations among integrating systems. Minimizing PAEs requires a greater

understanding of the causes of these events, especially in merging entities. As such, this

study provides a framework for policymakers to identify critical aspects of patient safety

and eliminate the structural features of health systems and the processes of care that lead

to adverse events. Given the recent emphasis on planning patient safety improvement

interventions targeting rural hospitals, I recommend more research is needed on patient

safety in those circumstances. In particular, the study findings justify the urgent

development of interventions designed to reduce the episode of unsafe healthcare

experienced by rural patients after mergers.

Patient Risks and Clinical Care Disruptions in Rural Hospital Mergers

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