Literature Review

Impact of Heath Information Technology on the Quality of Patient Care

Amanda Hessels, PhD MPH RN CIC CPHQ, Postdoctoral Research Fellow at the Center for Interdisciplinary Research to Prevent Infections (CIRI), Columbia University, School of Nursing and Nurse Scientist at Meridian Health in New Jersey

Linda Flynn, PhD RN FAAN, Professor and the Associate Dean of Academic Programs at the University of Colorado College Of Nursing

Jeannie P. Cimiotti, PhD RN FAAN, Associate Professor and the Dorothy M. Smith Endowed Chair at the University of Florida College Of Nursing

Suzanne Bakken, RN PhD FAAN FACMI, and Alumni Professor of Nursing and Professor of Biomedical Informatics at Columbia University

Robyn Gershon, MT (ASCP) MHS DrPH Professor of Epidemiology and Biostatistics and Core Faculty in the Philip R. Lee Institute for Health Policy Studies in the School of Medicine at University of California, San Francisco

Abstract

Objective—To examine the relationships among Electronic Health Record (EHR) adoption and adverse outcomes and satisfaction in hospitalized patients

Materials and Methods—This secondary analysis of cross sectional data was compiled from four sources: (1) State Inpatient Database from the Healthcare Cost Utilization Project; (2)

Healthcare Information and Management Systems Society (HIMSS) Dorenfest Institute; (3)

Hospital Consumer Assessment of Healthcare Providers and Systems Survey (HCAHPS) and (4)

New Jersey nurse survey data. The final analytic sample consisted of data on 854,258 adult

patients discharged from 70 New Jersey hospitals in 2006 and 7,679 nurses working in those same

hospitals. The analytic approach used ordinary least squares and multiple regression models to

estimate the effects of EHR adoption stage on the delivery of nursing care and patient outcomes,

controlling for characteristics of patients, nurses, and hospitals.

Results—Advanced EHR adoption was independently associated with fewer patients with prolonged length of stay and seven-day readmissions. Advanced EHR adoption was not associated

with patient satisfaction even when controlling for the strong relationships between better nursing

practice environments, particularly staffing and resource adequacy, and missed nursing care and

more patients reporting “Top-Box,” satisfaction ratings.

Conclusions—This innovative study demonstrated that advanced stages of EHR adoption show some promise in improving important patient outcomes of prolonged length of stay and hospital

readmissions. Strongly evident by the relationships among better nursing work environments,

HHS Public Access Author manuscript Online J Nurs Inform. Author manuscript; available in PMC 2016 August 26.

Published in final edited form as: Online J Nurs Inform. 2015 ; 19: .

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better quality nursing care, and patient satisfaction is the importance of supporting the

fundamentals of quality nursing care as technology is integrated into practice.

Keywords

electronic health records; nursing practice environment; adverse patient events; patient satisfaction; patient safety; health information technology

Introduction

The promise of advanced technology to transform healthcare is underway. We are in an

exciting and dynamic period of discovery, and importantly generating knowledge that

informs and impacts healthcare organizations, healthcare workers and ultimately patient

outcomes. Our innovative study adds to this body of knowledge by examining important and

untested relationships. The purpose of this study was to examine the relationships among

electronic health record (EHR) adoption stage and hospitalized patients’ satisfaction and

adverse outcomes (i.e., Patient Safety Indicators [PSIs], readmissions, length of stay and

prolonged length of stay [PLOS]) while accounting for important organizational and nurse

factors.

Background and Significance

Adverse events in hospitalized patients increase patient morbidity and mortality and are

costly to individuals, hospitals, and society. A report by the Institute of Medicine (IOM)

identified the top 100 healthcare research priorities for the nation; leading the list is research

aimed at improving patient safety and the quality of care (IOM, 2009). Yet, despite an

increased focus on patient safety since the release of the IOM report To Err is Human there has been minimal improvement in patient safety (IOM, 2012; Leape, et al., 2009; Wachter,

2010a, 2010b). Perhaps most disturbing are findings from a recent large, landmark study

which indicate that, despite national attention and substantial resource allocation, there has

been no reduction in the rate of preventable adverse inpatient events over the last several

years (Landrigan et al., 2010). In fact, the rate of preventable harm to patients has remained

relatively stable at 40.2 adverse events per 1,000 patient days (Landrigan et al., 2010). These

sustained rates of inpatient adverse events are detrimental to individuals, hospitals, and

society, costing our healthcare system more than 4.4 billion dollars per year (Department of

Health and Human Services (DHHS), 2010a).

Tolerance with this status quo is waning. Payers, regulators, insurers and consumers are

demanding the delivery of safe healthcare with positive outcomes. Consumer concern

became evident in a seminal 2006 national survey of public perspectives on ways to improve

healthcare in which 42 percent of respondents reported experiencing inefficient, poorly

coordinated or unsafe care in the prior two years (Schoen, How, Weinbaum, Craig & Davis,

2006). Concern remained evident in a 2011 international survey in which up to 25 percent of

U.S. respondents reported experiencing an actual error in care (Schoen et al; 2011).

Importantly, a consequence of low quality healthcare and poor work environments also

includes decreased patient satisfaction (Kutney-Lee et al., 2009; Mitchell & Shortell, 1997;

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Schubert et al., 2008). The confluence of these factors has led to a demand for healthcare

reform.

In response to this demand, the Affordable Care Act (ACA) of 2010 established the Hospital

Value Based Purchasing (VBP) program, a Center for Medicare and Medicaid Services

(CMS) initiative that rewards acute-care hospitals with incentive payments for the quality of

care provided (CMS, 2013). VBP places 2 percent of hospital Medicare reimbursement at

risk by metrics of quality, outcomes, and experiences of care (CMS, 2013). Reimbursement

associated with patient satisfaction is 30% of the at-risk base diagnosis-related group (DRG)

operating payment (CMS, 2013). The ACA affects payment for inpatient stays in 2,985 U.S.

hospitals (CMS, 2013).

To further support healthcare improvement the American Recovery and Reinvestment Act

(ARRA) of 2009 includes a provision for the Health Information Technology for Economic

and Clinical Health (HITECH) Act (CMS, 2012a, CMS, 2012b). The belief that health

information technology (IT) will foster healthcare reform is supported by a $35 billion

federal investment for HITECH programs, including demonstration of Meaningful Use

(MU), (DHHS, 2010b, Office of the National Coordinator (ONC), 2010). MU goals were

designed to occur in stages. The first phase, Stage 1 Meaningful Use (2011–2012), focuses

on data capture and sharing (ONC, 2012). The second phase, Stage 2 (2013–2014), advances

stage 1, and includes advanced clinical processes and clinical decision support, and focuses

on demonstrating health system improvement through wider adoption and process

improvement. The third phase, Stage 3 (2015), focuses on transforming health care through

health IT. Finally, beyond 2015, a learning system of transformed health care will be realized

(ONC, 2010).

Organizations that accept Medicare and Medicaid dollars are eligible to participate in the

Electronic Health Record (EHR) incentive programs and receive EHR incentive payments

beginning with a $2 million base payment, with over $5 billion paid to date (CMS, 2012b).

Eligible hospitals that do not minimally demonstrate MU Stage 1 will be subject to

Medicare penalty payment adjustments in 2015 (CMS, 2012b, DHHS, 2010a, HIMSS,

2012).

Fully meeting MU Stage 1 objectives includes three of five stages of EHR adoption (Appari,

Johnson & Anthony, 2013; Garets & Davis 2008; Jha et al., 2009), (Table 1). Hospitals at

EHR Stage 0 may have some clinical systems in place but are considered rudimentary and

do not have all three basic ancillary systems installed. Hospitals at EHR Stage 1 have

adopted all three core ancillary department information systems (laboratory, radiology,

pharmacy). Hospitals at EHR Stage 2 have adopted all of EHR Stage 1 applications and

additionally have features such as clinical data and decision support systems, clinical data

repository and may be health information exchange capable. Hospitals at EHR Stage 3 have

adopted all of EHR Stage 1 and EHR Stage 2 applications as well as nursing and clinical

documentation, order entry management and features such as electronic medication

administration record application and picture archive and communication systems MU Stage

2 includes hospitals at EHR Stage 4 that achieved all the preceding stages and have

Computerized Physician Order Entry (CPOE) and advanced clinical decision support

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(clinical protocols). This classification is based on the HIMSS Electronic Medical Record

Adoption Model (EMRAM) and the taxonomy developed by an expert consensus panel

(Garets & Davis 2008; Jha et al., 2009).

Undoubtedly, these Acts have challenged hospital administrators as they appraise the

evidence and formulate how to direct valuable human and material resources in efforts to

meet the provisions of both the ARRA and the ACA. The use of the health IT is one

promising system-level initiative that may improve provider performance and

interdisciplinary communication, reduce adverse patient events, and ultimately improve

patient satisfaction with care (Elnahal, Joynt, Bristol & Jha 2011; Himmelstein, Wright &

Woolhandler, 2010; Staggers, Weir & Phansalkar, 2008). Some evidence suggests that

technology does enhance communication and decision making and positively impacts

provider performance and a variety of patient outcomes, including patient satisfaction

(DesRoches, Miralles, Buerhaus, Hess & Donelan, 2011; Elnahal et al., 2011; Kazley,

Diana, Ford & Menachemi, 2012; Kutney-Lee & Kelly, 2011). However, an evidence report

published by the Agency for Healthcare Research and Quality (AHRQ) concluded too few

studies link organizational structures and care processes with outcomes when examining the

positive effects of EHR (Shekelle, Morton, & Keeler, 2006).

Despite widespread attention and funding, major gaps in the evidence persist, including

exploring the influence of EHRs across differing organizational climates, using relatively

small samples of hospitals, and the absence of any multi-site studies to disentangle the

complex relationships among EHR, the delivery of nursing care, and patient outcomes. By

leveraging existing databases, this study addressed these important gaps in the empirical

literature by exploring the relationships among EHR adoption stage, patient satisfaction, and

adverse patient outcomes while accounting for the important features of the nursing practice

environment, such as management support, teamwork and communication, and staffing, in a

sample of 70 New Jersey hospitals.

Objective

The purpose of this study was to examine the relationships among electronic health record

(EHR) adoption stage and hospitalized patients’ satisfaction and adverse outcomes (i.e.,

Patient Safety Indicators [PSIs], readmissions, length of stay and prolonged length of stay

[PLOS]).

Materials and Methods

A secondary analysis of cross sectional data was conducted, including the following

measures compiled from four sources: (1) adverse patient events and PSIs using PSI

algorithm (version 3.1) from the Healthcare Cost and Utilization Project, State Inpatient

Database; (2) patient satisfaction survey data from Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS), Centers for Medicare and Medicaid Services (CMS) data; (3) EHR adoption stage using the EMR Adoption Model (EMRAM) scale from

the Healthcare Information and Management Systems Society (HIMSS) Dorenfest Institute,

(Garets & Davis, 2008; HIMSS, 2008); and (4) nurse practice environment scores using the

Practice Environment Scale-Nursing Work Index (PES-NWI), (Lake, 2002), and missed

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nursing care scores from the New Jersey nurse survey data. All study data were from 2006,

with the exception of HCAHPS data with a release date of March 2008 which captures data

from July 2006 through June 2007. These years were selected so the data was

contemporaneous with the unique nursing variable dataset collected only 2006. The

databases were merged using unique hospital level identifiers. The study design included

adult patients admitted to New Jersey hospitals and nurses who were employed in those

same hospitals. Individuals under the age of 21 were excluded from this study as the focus

of the study was adult patients and nurses who are typically older than 21 years. No gender,

racial or ethnic groups were excluded.

Ethics Approval

The Institutional Review Board of Rutgers, The State University of New Jersey approved

this study.

Data Sources and Variables

Patients—Patient adverse events were derived from the 2006 New Jersey State Inpatient Database, which contains inpatient discharge abstracts and more than 100 clinical and

nonclinical data elements such as facility identification number, patient demographics,

admission and discharge information, payment source, total charges, and length of stay. In

addition, International Classification of Diseases, 9th edition, Clinical Modification (ICD-9- CM) codes are recorded for both the principal diagnosis and principal surgical procedures.

An expanded number of diagnosis and procedure codes and clear demarcation of presenting

and secondary (comorbid) diagnoses are unique and important features of the discharge data

that permit enhanced risk adjustment (Healthcare Cost and Utilization Project (HCUP),

2012a).

Nursing-sensitive PSIs that were examined included: (a) PSI 2 death in low-mortality

diagnostic related groups; (b) PSI 4 failure to rescue; (c) PSI 13 postoperative sepsis; (d) PSI

7 central venous catheter-related blood stream infection; and (e) PSI 8 postoperative hip

fracture. These PSIs were selected following review of empirical definitions (including

reliability and minimum bias, coding and construct validity, area level or provider level

metric), empirical performance indicating occurring at rates sufficient to detect a difference,

and literature review and theoretical rationale they are sensitive to nursing care (HCUP,

2012a, 2012b). Early hospital readmission was operationally defined as an all-cause

readmission to the same New Jersey hospital facility from which the patient was discharged

within seven days (HCUP, 2012a, 2012b). Prolonged length of stay (PLOS) identifies the

distribution point at which the discharge rate declines after the daily discharge rates peak

(Silber et al., 2009). The daily patient discharge rate was calculated as 1/LOS (length of

stay) consistent with previous work (Silber et al., 2003, Silber et al., 2009). The prolongation

point for hospital discharges, or day of deceleration, was identified by Kernel-Density plots

constructed for the discharge rates by each Major Diagnostic Categories and defined as the

day after the prolongation point. In these data, therefore, the patient’s hospital stay is

considered prolonged if it exceeds the prolongation point (day of hospitalization), identified

for each Major Diagnostic Categories by the Kernel–Density plots. All data were examined

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at the hospital level and therefore expressed as PSI rates per 1,000 discharges or percentage

of patient readmissions or PLOS per hospital.

Patient satisfaction was operationally defined as the hospital level average “Top-Box” score

from the HCAHPS hospital rating measure (CMS, 2012b). HCAHPS is a national,

standardized database of patients’ hospital experiences in short-term, acute care hospitals.

The 27-item survey includes categories on communication with doctors and nurses,

responsiveness of hospital staff, pain management, cleanliness and quietness of the hospital

environment, and medication and discharge instructions. It is reported as a set of ten

measures including 6 summary measures, 2 single items, and 2 global ratings. HCAHPS

“Top-Box” is defined as the most positive response to the HCAHPS survey questions,

including the response “9” or “10” for the overall hospital rating item (CMS, 2012b).

Individual patient responses are aggregated to the hospital level by HCAHPS following risk-

adjustment for patient mix and mode of administration (CMS, 2012b).

Nurses—The nursing practice environment was measured using the Practice Environment Scale of the Nursing Work Index (PES-NWI), a 5 domain, 31-item 4-point Likert-type

(ranging from strongly disagree to strongly agree) instrument that asks nurses to characterize

the presence of features in their work environment. Subscales from the PES-NWI used in

this study include: nurse participation in hospital affairs, nursing foundations for quality

care, nurse manager ability, leadership, and support of nurses, staffing and resource

adequacy and collegial nurse-physician relations (Lake, 2002). Published internal

consistency coefficients (Cronbach’s alphas) for these subscales range from .71 to .84 and

validity of this measure is extensively supported in the literature (Gajewski et al., 2010;

Lake, 2002; Lake, 2007; Lake & Friese, 2006; Liou & Cheng, 2010).

Nurses were also asked to report if any activities, from a set of 12 necessary care activities

were left undone during their last shift due to lack of time. The activities included: (1)

adequate surveillance (directly observation/monitoring) of patients, (2) teaching patients or

family, (3) preparing patients and families for discharge, (4) providing comfort/talk with

patients, (5) adequately document nursing care, (6) administering medications on time, (7)

skin care, (8) oral hygiene, (9) pain management, (10) treatment and procedures, (11)

coordinating care and (12) developing or updating nursing care plans. Construct validity of

this measure has been demonstrated in that scores have been found to be associated in the

theoretically expected direction with RN staffing, quality of care, and frequency of adverse

events in hospitals (Sochalski, 2001; 2004). Nurses’ reports of the work environment and

missed nursing care, although collected at the individual nurse level, are customarily

aggregated to produce a hospital-level metric as was done in this study (Aiken, Cimiotti,

Sloane, Neff & Flynn, 2011).

Hospitals—EHR adoption data were obtained from the 2006 HIMSS Analytic Database. HIMSS annually surveys a sample of U.S. nonfederal acute care hospitals including

independent hospitals and those within a healthcare delivery system. Providing data on more

than 5,100 hospitals, the HIMSS database is the most comprehensive collection of

information technology data and has been used in previous research on health IT (Kazley &

Ozcan, 2008; McCullough, Casey, Moscovice, & Prasad, 2010). EHR adoption was

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operationally defined as a hospital’s total cumulative score on the Electronic Medical

Record Adoption Model scale (EMRAM) ranging between 0–4 (Table 1) where a higher

score indicates more advanced adoption of technology (HIMSS, 2008).

Control variables—The potential confounding variables hypothesized to affect patient outcomes included nurse staffing levels, nurse education, hospital size, teaching status, high

technology status (defined as facilities with open-heart surgery, major organ transplant

services, or both), and geographic categories (Aiken, Clarke & Sloane, 2002; Aiken, Clarke,

Sloane, Sochalski & Silber, 2002; Appari, Johnson & Anthony, 2013; Elnahal, Joynt, Bristol

& Jha, 2011; Himmelstein, Wright & Woolhandler, 2010). These data were derived from the

New Jersey Nurse Survey and originally obtained from the American Hospital Association

Annual Survey. Patient risk-adjusted covariates were extracted from the State Inpatient

Databases (SIDS) and include age, sex, race, insurance type and ICD9-CM primary and

secondary diagnosis codes. The AHRQ risk adjustment method, based on the Elixhauser

method, was employed and includes a comprehensive set of 30 comorbidities (Elixhauser,

Steiner, Harris & Coffey, 1998).

Data management and analysis—Prior to analysis, all datasets were aggregated to the hospital level. The final analytic sample included 854,258 patients and 7,679 nurses in 70

New Jersey hospitals. The relationship between potentially confounding variables (control

variables) and their respective dependent variables were examined using bivariate Pearson or

Spearman correlations, as determined by the Shapiro-Wilk test of normality. Those showing

significant relationships (p < .05) were retained for inclusion in the multivariable models as control variables. The presence of multicollinearity was identified by variance inflation

factor diagnostics (VIF >10). In such cases, only one variable was included from the set of

correlated variables. Following these steps, the number of variables retained in all

multivariable models was based on rules for regression modeling (Harrell, 2001). Because

nurse, patient, and EHR data were clustered in hospitals, appropriate statistical methods for

analyzing clustered data were employed (Wears, 2002).

Data were assessed for outliers and missing data. Data on the key variables EMRAM,

nursing practice environment, missed nursing care, PSIs and PLOS were available for 70

New Jersey hospitals. In 2006, 51 hospitals submitted readmissions data, and two were

excluded from the readmission models due to incomplete data. All 41 hospitals that

submitted HCAHPS data were included in the patient satisfaction models. Ordinary least

squares and multiple regression models were used in the analytic approach to estimate the

effects of EHR adoption stage on the delivery of nursing care and patient outcomes and

controlled for characteristics of patients, nurses, and hospitals. Simple unadjusted OLS

regression models were used, followed by adjusted models using the retained control

variables identified by the steps previously described. These models were assessed for

heteroskedasticity, run with robust standard errors (Huber-White) if indicated, and residuals

were examined. Data were analyzed using STATA/MP 12.1 software. The level of

significance for testing was set at .05 and standardized coefficients (β) are reported.

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Results

Patient, Nurse, and Hospital Characteristics

The final analytic sample and unit of analysis was 70 New Jersey hospitals; data was

available from 854,258 patients and 7,679 nurses. The majority of study patients were male

(59 percent), white (66 percent), and insured (83 percent) with an average age of 59 years.

The most common comorbidities were chronic pulmonary disease (15.5%), uncomplicated

diabetes (15.4 percent) and fluid and electrolyte disorders (15.2 percent). Slightly more than

half of the nurses held specialty certification (52 percent), nearly half earned a BSN degree

(44%), and they cared for, on average, six patients per shift. The majority of the 70 hospitals

included in this study were below EMRAM Stage 3 (63 percent), had 250 beds or more (52

percent), were not high technology (75 percent), and were either non-teaching (46 percent)

or minor teaching hospitals (43 percent).

EHR Adoption Stage and Adverse Outcomes

The unadjusted effect of testing the relationship between EHR adoption stage and the patient

outcome of readmission within seven days was significant (R2 = .09, F (1, 47) = 4.70, p = . 03). Bivariate correlations did not significantly identify any potential confounders that

required additional testing using adjusted models. However, the Breusch-Pagan test

demonstrated evidence of heteroskedasticity (p < .01); thus, the model was conservatively estimated with robust standard errors and was not significant (p = .06) (Table 2).

The unadjusted effect of testing the relationship between EHR and PLOS was not significant

(R2 = .003, F (1, 68) = 0.21, p = .65). However, when adjusting for control correlates of PLOS (patient comorbidity, patient age, nurse staffing, and hospital technology status), the

adjusted effect was significant (R2 = .462, F (4, 63) = 6.54, p < .01), with EHR adoption stage being a significant contributor (β = −.21, p = .03). For every standard deviation unit (SD = 1.39) increase in EHR adoption stage, PLOS decreased by .21 standard deviation units (SD = 0.05). EHR adoption stage was not a significant predictor of other adverse outcomes.

EHR Adoption Stage and Patient Satisfaction

There was a statistically significant relationship between EHR adoption level and patient

satisfaction in acute care hospitals in one of the ten patient satisfaction outcomes: “yes,

given discharge information.” The model included the control variables of patient race,

being insured, and nurse staffing. Findings indicate that the overall model was significant

(R2 = .04, F (4, 36) = 7.56, p < .01), with EHR adoption stage significantly contributing to this outcome (β = −.31, p = .02). However, this finding was in the inverse direction and indicated that higher EHR adoption stages were predictive of lower percentages of patients

who responded “yes, given discharge information” (Table 3).

As the primary purpose of the study was to examine the effect of EHR adoption levels on

patient outcomes, and because the evident and strong relationship among the nursing

practice environment and missed nursing care and outcomes might confound this

relationship, these variables were controlled to isolate the effect of EHR. Models were

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constructed to examine with greater precision whether advanced EHR technology is

positively related to patient satisfaction outcomes by controlling for the statistically

significant effects of the nursing practice environment and missed nursing care. The nursing

practice environment dimension of staffing and resource adequacy was specifically tested

secondary to the evident relationship between this dimension of the nursing work

environment and patient satisfaction. Results indicate that higher EHR adoption stages were

predictive of one satisfaction outcome, lower percentages of patients who respond “yes,

given discharge information” (β = −.27, p < .05) when the strong relationships among the nursing practice environment and missed care and satisfaction were held constant (Table 4).

Discussion

Findings from this study suggest that an inverse relationship exists between EHR adoption

stage and the patient outcomes of PLOS and readmissions. The findings of this study did not

suggest that increased EHR adoption stages are related to decreased adverse outcomes of

PSIs outcomes or increased patient satisfaction. Additional analysis was conducted to

examine if the strong relationships among the nursing work environment, missed nursing

care and patient satisfaction were confounding the effect of EHR on patient satisfaction

outcomes. Notably, in the final adjusted models only one satisfaction outcome, the patient

response of “being given discharge information,” reached the level of statistical significance.

This relationship, however, was in the opposite direction of that theorized.

These important relationships have not been tested in prior studies, and as such these novel

findings may indicate that at the EMRAM stages 0–4 of EHR adoption, the patient

satisfaction benefit is tempered by staffing and resource adequacy. There is little to no extant

theoretical or empirical support for this unexpected finding. One possible explanation is that

the relationship between EHR and satisfaction may be moderated by insufficient resources,

which in the presence of new technology has the effect of reducing workflow and time

efficiencies (Huber, 1990; Poissant, Pereira, Tamblyn & Kawasumi, 2005).

Methodologically, it is unknown if achievement of these EHR adoption stages is new in

these settings; consequently the impact on nursing processes of care and workflow is

unknown. In order to optimize the positive effect of EHR on patient outcomes,

organizational strategies and resources must be committed to ease and guide the transition to

this technology (Huber, 1990; Walker et al., 2008). Although this study accounted for

organizational factors that may serve as indicators of available resources (teaching status,

hospital size, geographic location, and technology status), the comprehensive nature and

extent of the organizational strategy to implement EHR technology was unknown.

Importantly, these findings suggest that sufficient staffing and resources, as rated by the

nurses, are essential for advanced EHR adoption and patient reported outcomes of

satisfaction. These findings are consistent with extant literature and may also suggest that

patient benefits of advanced technology will only be realized in context of sufficient human

resources (Furukawa, Raghu & Shao, 2011; Jha et al., 2009; Kazley & Ozcan, 2007; Walker

et al., 2008). These novel findings warrant further investigation.

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Implications

The implications of the knowledge generated by this study are significant for patients,

nurses, administrators, and policy makers, particularly in context of the shifting healthcare

delivery and nursing practice landscape. Across the U.S., hospitals and nurses have made

significant efforts to achieve higher MU stages. Despite marked progress, nearly 30 percent

of U.S. hospitals that submitted data to HIMSS in 2015 remain at or below EMRAM middle

stage (3) of adoption, equivalent to Meaningful Use Stage 1 objectives (Appari et al., 2013;

HIMSS, 2015). In this 2006 study of New Jersey hospitals, 37% had achieved EMRAM

stage 3; one-third of these achieving the next cumulative level of EMRAM stage 4. Thus,

findings from this study are highly relevant and timely as we are in a period of rapidly

accelerating advancement and adoption.

These EMRAM stages correspond to MU Stages 1 and 2; thus, there are significant and

timely implications of these study findings for both current and future nursing practice and

hospital payment. Though the data indicate progress as the majority of hospitals in the New

Jersey 2006 baseline data were below EMRAM stage 3 while more recent 2015 national

data indicates approximately 30 percent remain at or below stage 3, a possible critical point

may not be reached to fully demonstrate the potential impact of EHR, not only in New

Jersey but across the United States.

Importantly, achieving EMRAM stage 3, including nursing documentation which is the

primary mechanism of electronic communication, is essential for safe transitions of care. As

such, outcomes that are more sensitive to good communication and care transitions, such as

readmissions, PLOS, and patient reports of “yes, given discharge information,” may

conceivably be early indicators of the impact of advanced EHR adoption. Implications for

hospital administrators and nursing practice are evident and congruent with best practice

guidelines, suggesting the engagement of nursing staff in the development, use, and ongoing

feedback of documentation systems and allocation of resources for ongoing training and

systems evaluation and improvement are necessary to optimize the system (Blavin, Ramos,

Shah & Devers, 2013). These findings suggest that multi-level interventions are required for

improving patient care and outcomes. This study demonstrates that EHR adoption does have

a positive, adjusted effect on PLOS, and it is theoretically plausible that, as features of

advanced technology become embedded in hospitals and other healthcare organizations,

positive benefits may extend to additional patient outcomes and institutional settings across

the continuum of care (Huber, 1990; Powell-Cope et al., 2008).

In summary, the dual demands of the legislative provisions to implement health IT and

improve quality outcomes may exacerbate the difficult decisions hospital administrators

need to make regarding allocation of valuable resources. There is a strong financial incentive

to integrate technology into the healthcare work environment and sound theoretical rationale

to believe that through enhanced communication, improved data management and better

transitions of care that EHRs will benefit patients and providers alike. Broader implications

of these study findings for administrators suggest organizations that have strong

fundamentals of quality nursing care in place may realize improved patient satisfaction

outcomes that translate into real dollars through the VBP program. Additionally,

implications of this study for administrators and policy makers suggest that meeting the

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demands of the ARRA and ACA may not be mutually exclusive. Rather, in an iterative

manner, a supportive nursing work environment that is adequately staffed and resourced may

improve patient satisfaction, leading to better organizational financial health. These fiscal

resources can in turn be used by organizations to continue advancing EHR adoption, EHR

implementation, and the transformation of health care in the U.S.

Limitations

Despite these novel findings and important implications, several limitations of this study

should be acknowledged. This study was cross-sectional and as such correlations,

relationships, and associations between variables of interest were examined, but causality

could not be ascertained. The precision of the PSI data was dependent on the documentation

in the record and coding applied by trained medical coders; thus, discrepancies in data and

accuracy could have existed at the hospital level (AHRQ, 2004, 2010; Zhan & Miller, 2003).

Methodologically, patient responses cannot be linked temporally to specific hospital EHR

adoption timelines. This study was designed to mitigate this possible limitation by including

data from HCAHPS release date of March 2008, which captures data from July 2006

through June 2007. However, it remains unknown if this negative finding may in part reflect

early EHR adoption and the attendant human factors and operational challenges thought to

effect the use of this technology and subsequent proposed benefits, by nurses and patients

alike (Powell-Cope et al., 2008). EHR data were obtained from HIMSS and patient

satisfaction from HCAHPS; both are voluntary reporting systems, and as such these data

were subject to self-selection bias. Finally, analysis at the hospital level limits sample size,

and though the power analysis indicated the sample size was sufficient and significant

effects identified, the sample size of hospitals in the patient satisfaction models may have

been a limitation.

Conclusions

In this study of 70 acute care hospitals, higher levels of EHR adoption were significantly and

independently associated with fewer incidences of PLOS and partially associated with lower

rates of seven-day re-hospitalization. This is the first study that examined this relationship

between EHR adoption stage and PLOS, thus extending this knowledge. These findings

support the promising role of EHRs in improving patient outcomes. Importantly, however,

findings also indicate that a supportive nursing practice environment, including the domain

of adequacy of nursing staff and resources, is significantly and independently associated

with lower levels of missed nursing care and higher levels of patient satisfaction, with and

without adjusting for EHR levels. Thus, findings from this study indicate that a multi-faceted

approach that includes technology, such as EHR, as well as system-wide nursing supports

are needed to improve patient care outcomes in acute care hospitals. In summary, these

findings add to a growing body of knowledge in nursing research that identifies modifiable

technologic and nursing-focused system factors that are critical to improving patient care

and outcomes.

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Biographies

Amanda Hessels, PhD, MPH, RN, CIC, CPHQ is a Postdoctoral Research Fellow at the

Center for Interdisciplinary Research to Prevent Infections (CIRI), Columbia University,

School of Nursing and Nurse Scientist at Meridian Health in New Jersey. Her program of

funded research examines the relationships among organizational factors, processes of

nursing care and adverse patient outcomes in acute care settings. She has used both primary

survey methodology and existing large-scale datasets to advance knowledge of these

relationships.

Linda Flynn, PhD, RN, FAAN

Dr. Linda Flynn is a Professor and the Associate Dean of Academic Programs at the

University of Colorado College Of Nursing. Her program of funded research, using large-

scale survey methodology, focuses on the impact of system factors on nurse, patient, and

faculty outcomes across a variety of settings. She is the author of multiple peer-reviewed

publications and her research has influenced policy decisions at the state and national levels.

Jeannie P. Cimiotti, PhD, RN, FAAN

Dr. Cimiotti is Associate Professor and the Dorothy M. Smith Endowed Chair at the

University of Florida College Of Nursing. She is internationally known for the development

and implementation of health care surveys and managing large health care datasets. Dr.

Cimiotti’s research examines the organizational features of hospitals that lead to poor patient

care outcomes.

Suzanne Bakken, RN, PhD, FAAN, FACMI is the Alumni Professor of Nursing and

Professor of Biomedical Informatics at Columbia University. She directs the Center for

Evidence-based Practice in the Underserved and the Reducing Health Disparities Through

Informatics pre- and post-doctoral training program. She has been conducting federally-

funded informatics research for more than 25 years. Dr. Bakken currently serves as the

President of the American College of Medical Informatics.

Robyn Gershon, MT (ASCP), MHS, DrPH is a Professor of Epidemiology and Biostatistics

and Core Faculty in the Philip R. Lee Institute for Health Policy Studies in the School of

Medicine at University of California, San Francisco. She is also an Adjunct Professor at the

School of Nursing, UCSF, and Adjunct Professor at University of California, Berkeley. She

is a nationally recognized researcher in the area of occupational and environmental health,

specializing in public health disaster preparedness and response and in the health care

workplace and workforce.

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Table 1

Stages of Electronic Health Record Adoption Used in This Study*

Stage Cumulative Capabilities

0 All three ancillaries not installed: laboratory, radiology, pharmacy

1 All three 3 ancillaries installed: laboratory, radiology, pharmacy

2 Clinical data repository (CDR), controlled medical vocabulary, clinical decision support system (CDSS), health information exchange (HIE) capable, may have document imaging and Stage1 applications

3 Nursing and clinical documentation (flow sheets), CDSS (error checking), picture archiving and communication systems (PACS) available outside of radiology and Stage 1 and 2 applications

4 Computerized Physician Order Entry (CPOE), Clinical decision support (clinical protocols) and Stage 1, 2, and 3 applications

* Stages are from the HIMSS Analytics EMR Adoption Model

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c er

ti fi

ca ti

on , e

du ca

ti on

; pa

ti en

t co

m or

bi di

ti es

, a ge

, r ac

e, g

en de

r an

d in

su ra

nc e

st at

us ;

an d

ho sp

it al

g eo

gr ap

hi c

lo ca

ti on

, s iz

e (b

ed s)

, h ig

h te

ch no

lo gy

s ta

tu s

an d

te ac

hi ng

s ta

tu s.

Online J Nurs Inform. Author manuscript; available in PMC 2016 August 26.

A u th

o r M

a n u scrip

t A

u th

o r M

a n u scrip

t A

u th

o r M

a n u scrip

t A

u th

o r M

a n u scrip

t

Hessels et al. Page 19

Ta b

le 4

E ff

ec ts

o f

E H

R , A

dj us

te d

fo r

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ff in

g an

d R

es ou

rc es

a nd

M is

se d

N ur

si ng

C ar

e on

P at

ie nt

S at

is fa

ct io

n (N

= 4

1)

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ad ju

st ed

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ju st

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ar ia

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β t

p β

t p

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0 .0

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80

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* p <

.0 5

N ot

e: A

ll r

es po

ns es

a re

“ T

op -B

ox ”

or r

es po

ns e

of “

al w

ay s”

o r

hi gh

es t

po ss

ib le

r at

in g.

A dj

us te

d m

od el

s in

cl ud

ed i

nd ic

at ed

c on

tr ol

v ar

ia bl

es n

ur se

s ta

ff in

g le

ve ls

, c er

ti fi

ca ti

on , e

du ca

ti on

; pa

ti en

t co

m or

bi di

ti es

, a ge

, r ac

e, g

en de

r an

d in

su ra

nc e

st at

us :

an d

ho sp

it al

g eo

gr ap

hi c

lo ca

ti on

, s iz

e (b

ed s)

, h ig

h te

ch no

lo gy

s ta

tu s

an d

te ac

hi ng

s ta

tu s.

Online J Nurs Inform. Author manuscript; available in PMC 2016 August 26.