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JONA Volume 40, Number 7/8, pp 336-343 Copyright B 2010 Wolters Kluwer Health | Lippincott Williams & Wilkins

T H E J O U R N A L O F N U R S I N G A D M I N I S T R A T I O N

Achieving ‘‘Meaningful Use’’ of Electronic Health Records Through the Integration of the Nursing Management Minimum Data Set

Bonnie L. Westra, PhD, RN, FAAN

Amarnath Subramanian, MD, MS

Colleen M. Hart, MS, RN

Susan A. Matney, MS, RN-C

Patricia S. Wilson, RT(R), CPC, PMP

Stanley M. Huff, MD

Diane L. Huber, PhD, RN, FAAN, NEA-BC

Connie W. Delaney, PhD, RN, FAAN, FACMI

Objective: To update the definitions and measures for the Nursing Management Minimum Data Set (NMMDS). Background: Meaningful use of electronic health records includes reuse of the data for quality im- provement. Nursing management data are essential to explain variances in outcomes. The NMMDS is a research-based minimum set of essential standard- ized management data useful to support nursing man- agement and administrative decisions for quality improvement. Methods: The NMMDS data elements, definitions, and measures were updated and normalized to cur- rent national standards and mapped to LOINC (Logi- cal Observation Identifier Names and Codes), a federally recognized standardized data set for pub- lic dissemination. Results: The first 3 NMMDS data elements were up- dated, mapped to LOINC, and publicly disseminated.

Conclusions: Widespread use of the NMMDS could reduce administrative burden and enhance the mean- ingful use of healthcare data by ensuring that nurs- ing relevant contextual data are available to improve outcomes and safety measurement for research and quality improvement in and across healthcare organizations.

The anticipated cost savings associated with health- care reform are in part predicated on the assump- tion that meaningful use of electronic health records (EHRs) can streamline care processes and increase the reuse of clinical and administrative data to im- prove patient safety and outcomes and increase ac- cess to care. Beginning in October 2010, the Centers for Medicare and Medicaid Services will provide Medicare incentive payments to hospitals and pro- viders who meet the criteria for meaningful use of EHRs, and reimbursement will decrease in 2015 for those who do not meet the criteria.1 The meaningful-use criteria include electronic documen- tation of care and exchange of data across orga- nizations as well as reuse of the data for quality improvement. The single most important resource in reforming the healthcare system is the need for ac- curate, representative, and relevant data regarding information pertaining to patient needs, care pro- vided, outcomes realized, and information about the appropriate use of resources influencing care. Given that nurses constitute the largest group of healthcare professionals in the United States,2 it is vital that

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Authors’ Affiliations: Assistant Professor (Dr Westra), Professor and Dean (Dr Delaney), Doctoral Student (Ms Hart), School of Nursing, University of Minnesota, Minneapolis; Medical Director (Dr Subramanian), Department of Pathology, Health Partners, Bloomington, Minnesota; Doctoral Student (Ms Matney), Office of the Associate VP for Health Sciences Information Technology, University of Utah, Salt Lake City; Senior Content Engineer (Ms Wilson), 3M Health Information Systems Incorporated, Murray, Utah; Clinical Professor Biomedical Informatics (Dr Huff), Inter- mountain Health Care, Salt Lake City, Utah; Professor (Dr Huber), College of Nursing, University of Iowa, Iowa City.

Corresponding author: Dr Westra, University of Minnesota, School of Nursing, WDH 5-140, 308 Harvard St SE, Minneapolis, MN 55455 ([email protected]).

DOI: 10.1097/NNA.0b013e3181e93994

appropriate nursing clinical and contextual infor- mation is captured, stored, and linked with other healthcare data to evaluate and continuously shape ongoing system changes.

The process of quality improvement is shifting from review of paper charts to reuse of data from data warehouses, which contain extracts of data from com- puterized systems such as billing and claims data and, more recently, from EHRs. However, management and administrative data that describe the context of care and care delivery are missing in these reposi- tories. Organizational variables, provider and work- force characteristics, and financial data that represent the context of nursing care influence the effective- ness of care delivery and patient outcomes.3 Nursing management data are collected in every healthcare setting; however, if the data are not captured in data warehouses and/or lack consistency in definitions and coding, it is impossible to reuse these manage- ment data to compare patient outcomes and nursing workforce issues within and across settings. These data need to be standardized and included in data warehouses along with EHR clinical data to meet the criteria for ‘‘meaningful use’’ of EHRs.

The Nursing Management Minimum Data Set (NMMDS) is a research-based minimum set of essential data elements that can fill the void in data warehouses to describe the management of nursing care.4 The NMMDS was developed over 10 years ago and is available as a paper-based survey upon request from the developers. With the increased em- phasis on quality improvement through reuse of EHR data, it is essential to update and publicly distribute the standardized NMMDS data elements, definitions, measures, and codes to complement EHR data. The distribution of the NMMDS is best accomplished by linking it to a federally accepted national terminology that is publicly available. The Logical Observation Identifier Names and Codes (LOINC) system is one such standard with a history of incorporating survey instruments. In this article, the investigators describe the methods and outcomes of the initial steps to update the definitions and measures for 3 of the 18 NMMDS data elements, normalize these measures to current national stan- dards, and disseminate the data set by linking the NMMDS to LOINC.

Background Literature

Nursing Management Minimum Data Set

The NMMDS is a research-based minimum set of essential data elements for capturing unit- or service- level nursing management data that are accurate, reliable, and useful for management decision making.

It is composed of 18 data elements organized in 3 cat- egories: environment, nursing care, and financial re- sources, as shown in Table 1. Each NMMDS data element is operationalized by more specific subcon- cepts and measures that can be linked with nursing management data already collected.

The NMMDS development initially began in 1989. Donabedian’s structure, process, and outcome framework5; the Iowa Model of Nursing Admin- istration; and the USA Nursing Minimum Data Set served as conceptual foundations for the data set. Multiple studies, one of which was supported by the American Organization of Nurse Executives, were conducted to develop and establish validity of the NMMDS data elements and definitions. Validity was established first in acute-care settings and then in long- term-care settings, ambulatory clinics, and community settings.5 In 1998, The American Nurses Association recognized the NMMDS as 1 of 2 data sets and 10 terminologies for nursing.

The value of the NMMDS is that it identifies nursing management variables that can be combined with billing and clinical data to build a better un- derstanding on how nursing resources and the context of care influence patient safety and other outcomes. Moreover, the NMMDS can foster an increased un- derstanding of the nursing workforce needs in terms of quantity and level of expertise specific to specialties and settings of care. The NMMDS has not been implemented in its entirety within the United States for comparison of nursing management data across settings or extensively included in data warehouses. Consequently, access to these data to support qual- ity improvement activities or research is minimal. There is beginning research on incorporating nursing

Table 1. Nursing Management Minimum Data Set Variables and Definitions

NMMDS: Environment NMMDS: Nursing Care Resources

1. Unit/service unique identifier

11. Management demographic profile

2. Type of nursing delivery unit/service

12. Staffing

3. Patient/client population 13. Staff demographic profile

4. Volume of nursing delivery unit/service

14. Satisfaction

5. Nursing delivery unit/ service accreditation

6. Autonomy

NMMDS: Financial Resources

7. Environmental complexity

15. Payer type

8. Patient/client accessibility

16. Reimbursement

9. Method of care delivery

17. Nursing delivery unit/ service budget

10. Clinical decision-making complexity

18. Expenses

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management in data warehouses.6 Specific elements of the NMMDS have proven valuable for under- standing costs of care,7 impact of staff turnover,8

adverse events,9 and patient morbidity and mortal- ity.10,11 Many of the NMMDS variables have been in- corporated into the Magnet Recognition ProgramA, the National Database of Nursing Quality Indi- cators, the National Quality Forum, and The Joint Commission quality indicators. However, these ef- forts are focused primarily on acute-care settings. The NMMDS, on the other hand, has a broader ap- plication as it is designed to be used in any healthcare setting. There is a need to update and harmonize the NMMDS data elements with current national nurs- ing quality efforts, health data standards, and re- search as well as to disseminate the results through a publicly available tool. The LOINC was chosen as the national data standard to link and dissem- inate the NMMDS data elements because data struc- tures are similar, and as mentioned previously, the LOINC has been specifically used to incorporate sur- vey instruments.

Logical Observation Identifier Names and Codes

The LOINC terminology is a publicly available, no- cost database that provides a set of universal names and codes with a similar structure to the NMMDS. The structure of LOINC includes a name-value pair, equivalent to a question or observation requiring the user to record an answer. LOINC can be used in computer databases and provides a national struc- ture for transmitting data in electronic messages.12

LOINC was developed through funding by the Na- tional Library of Medicine and the Agency for Health- care Policy and Research beginning in 1994 at the Regenstrief Institute, a research foundation affiliated with the Indiana University School of Medicine.13

Major goals of LOINC are to create user-friendly categories of terms, definitions, and codes that are universally used by all information systems to fa- cilitate data exchange and use within and across healthcare organizations. LOINC is recognized by the American Nurses Association14 and the US De- partments of Health and Human Services as a uni- form standard for the electronic exchange of clinical health information and adopted by the National Com- mittee on Vital Statistics for electronic exchange of laboratory results.15

Methods

The first 3 data elements of the 2005 version of the NMMDS were evaluated for (1) usefulness, (2) logi- cal organization, (3) consistency with health data standards and research, (4) clarity of conceptual and

operational definitions, and (5) the data structure for linking with LOINC. An iterative process was used to evaluate each data element. Existing standards and the literature were reviewed for conceptual and op- erational definitions. A resulting list of resources was compiled, and recommendations presented to the re- search team for consensus on the final definitions. Each NMMDS data element, subconcept, and mea- sure was entered in Excel, and a proposed LOINC coding was developed. A small group from the na- tional LOINC committee reviewed the definitions and the proposed LOINC coding before presenting the fi- nal updated NMMDS data elements, definitions, and coding to the full national LOINC committee for ap- proval. Once approved, the revised NMMDS data ele- ments with LOINC codes were incorporated into the next release of LOINC for public distribution and the next version of the NMMDS.

Results

Results are reported separately for each of the NMMDS data elements with examples of the mea- sures. The full list of measures is available on the University of Minnesota School of Nursing’s Interna- tional Classification of Nursing Practice Center for Nursing Minimum Data Set Knowledge Discovery under ‘‘USA NMMDS Updates’’ (http://www.nursing. umn.edu/ICNP/USANMDS/home.html).

NMMDS 01: Unit/Service Unique Identifier

The Unit/service unique identifier was defined in 2005 as the unique name, identifier, payment and geographic data for a center of excellence, service program, cluster by level of care, service/product line, or service/area where the majority of patient/client care is delivered; this is the first level of data aggregation beyond the patient/client care provider and included 9 subconcepts. The original subcon- cepts were unique facility identifier, unique service identifier, unique service name, unique unit identifier, unique unit name, Medicare payment category, geo- graphic location, postal location, and country code. Of the original subconcepts, 3 were retained but up- dated, 6 were retired, and 3 new subconcepts added for a total of 6 subconcepts in the updated version. The unique facility identifier, geographical location, and postal code were retained and updated. Existing governmental standards were used to provide mea- sures for these, and the coding available from the gov- ernment Web sites is referenced so that as the codes change, the measures for the NMMDS data also are updated, supporting consistency in data elements and coding over time. The place of service also includes 2 for ‘‘stores’’ and ‘‘voluntary health or charitable

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agencies’’ that are not in the national governmental standards; these are important to include as these are places where nurses practice. Software vendors and health information technology staff track or receive notices about changes in government stan- dards so they can continuously update their soft- ware; thus, the NMMDS coding also is updated simultaneously within systems that use these vari- ables. Two new subconcepts were added: reporting period and facility name. The reporting period can be any 2 dates during which data are collected, that is, monthly, quarterly, or annually. Previously, dates for reporting NMMDS data were not included, nor was the name of the facility. A comparison between

the previous 2005 version and the 2009 version is shown in Table 2.

NMMDS 02: Nursing Delivery Unit or Service

The nursing delivery unit or service can be any ser- vice program (product line) or physical area where care is delivered. It is the first level of aggregation beyond the individual patient/client care provider.16

The subconcepts in the 2005 version contained 37 names for types of units or services; these codes were retired and replaced with codes included in the National Database for Nursing Quality Indicators (https://www.nursingquality.org/Documents/Public/ APPENDIX%20D.pdf). Mapping to an existing

Table 2. Comparison of the Previous and New Versions for NMMDS 01: Facility Unique identifiers

Previous NMMDS 01: Unit/Service Unique Identifier New NMMDS 01: Facility Unique Identifiers

A facility is the highest level of an organization for data aggregation for which unit-level data are reported. In some cases, a facility is the same as a unit if there is only 1 unit.

A facility is the highest level of an organization for data aggregation for which unit level data are reported. In some cases, a facility is the same as a unit if there is only 1 unit.

01.01 Unique facility identifier 01.01 Unique facility identifierVthe National Provider Identifier (NPI) is a unique identification number for healthcare providers specified by HIPAA. For the NMMDS, the NPI for organizations will be used to indicate the place that sends the bill. https://nppes.cms. hhs.gov/NPPES/Welcome.do

01.02 Unique service identifier Moved to 02.01 unique unit identifier, which includes both service or unit identifier

01.03 Unique service name Moved to 02.02 unique unit name, which includes both service or unit name

01.04 Unique unit identifier Moved to 02.01 unique unit identifier, which includes both service or unit identifier

01.05 Unique unit name Moved to 02.02 unique unit name, which includes both service or unit name

01.06 Medicare payment category Retired since geographical location and postal location capture the essence of this information

01.07 Geographic location (state, province, country) 01.07 Geographic locationVstate or territory of the facility where the service was provided or originated as defined by the US Postal Service (http://www.itl.nist.gov/fipspubs/)

01.08 Postal location (mailing code, zip code) 01.08 Postal location (zip code)Vzip code of the facility where service was provided or originated as defined by the US Postal ServiceVuse a 9-digit code if possible (http://zip4. usps.com/zip4/citytown_zip.jsp)

01.09 Country code RetiredVthis version of the NMMDS will focus on the US only at this time

No previous subconcept 01.10 Place of serviceVplace of service is the location, as indicated on healthcare professional claims forms, where the service was provided or originated. It is represented by 2-digit codes as defined by Centers for Medicare and Medicaid Services (CMS). (http://www.cms.hhs.gov/ PlaceofServiceCodes/Downloads/placeofservice.pdf). The NMMDS uses the CMS list plus additional codes that end with an ‘‘x.’’ 1X StoresVthese may include grocery, pharmacy, department,

or other stores where retail goods and merchandise are sold 2X Voluntary health or charitable agencies (eg, National

Cancer Society, National Heart Association, Catholic charities) No previous subconcept 01.11 Reporting periodVstarting through end date for

the period in which events occurredVnot when the data are collected or reported 01.09.01 Start date/time 01.09.02 End date/time

No previous subconcept 01.12 Facility name

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national standard used in 1,500 hospitals allows comparison of data collected by any other nursing setting that uses the NMMDS. The 4 subconcepts for the service and unit identifiers and names originally included in the NMMDS 01 data element were com- bined into 2 measures for the unique unit/service iden- tifier and name. Table 3 shows a comparison between the 2005 and 2009 version for NMMDS 02: nursing delivery unit or service data element.

NMMDS 03: Patient or Client Population

The NMMDS data element 03: patient or client population describes the characteristics of the popu- lation served by a nursing delivery unit or service. Originally, there were 4 subconcepts for this data element: specialty, developmental focus, interaction focus, and population focus. Of these 4 subconcepts, 1 was retired, 2 were retained with new names and updated measures, and 1 subconcept was moved to another NMMDS data element. When we compared the original measures for the population specialty, the measures were redundant with measures describing the type of unit in the previous data element; hence, this subconcept was retired. There were 2 subcon- cepts that were renamed for clarity. Developmental focus was renamed chronological age, and popula- tion focus was renamed catchment area. Develop- ment stages were used to measure the percentage of patients served on a unit by the developmental focus; however, developmental stages have changed over time, were not sufficiently detailed to describe the age of the population served, and included over-

lapping groups. Measures for chronological age were changed to 5-year incremental age categories plus ‘‘fetal’’ and ages ‘‘1-28 days.’’ There is no national standard for grouping patients by age. The National Cancer Institute’s age grouping was selected as it provided the smallest increments for age that would be applicable across any unit or service (http://www. seer.cancer.gov/stdpopulations/stdpop.19ages.html). To prevent redundancy in the NMMDS measures, the type of client served was moved to the NMMDS 04: volume of nursing care. The volume of nursing care includes calculations for hours of care by type of client, type of nurse provider, and type of encounter. Finally, a new subconcept to capture the total popu- lation served during a reporting period was added for comparison of the size of a unit or service and be- comes the denominator when calculating percentages of clients served by age or catchment area. The 2005 and 2009 version of the NMMDS 03: patient or cli- ent population data element, subconcepts, and mea- sures are shown in Table 4.

Discussion

During the initial phase of this study, the inves- tigators examined the usefulness, clarity, and con- sistency of conceptual and operational definitions with governmental and health data standards and research, logical organization, and the data structure requirements for linking the NMMDS with LOINC. The first 3 NMMDS data elements were reworded, reorganized, redefined, and harmonized with existing

Table 3. Comparison of the Previous and New Versions for NMMDS 02: Nursing Delivery Unit or Service

Previous NMMDS 02: Type of Nursing Delivery Unit/Service New NMMDS 02: Nursing Delivery Unit or Service

Identify the unique name, identifier, and type of nursing unit or service for each component of the facility.

The unique name, identifier, and type of nursing unit or service for each component of the facility

01.02 Unique service identifier and 01.04 unique unit identifier 02.01 Unique unit identifierVan identifier given to a cost center by the facility for a unit, which only has meaning within the facility; this is the first level of data aggregation beyond the individual patient or care provider

01.03 Unique service name and 01.05 unique unit name 02.02 Unique unit nameVthe name assigned to a unit by the facility, which only has meaning within the facility

02.01-02.37 Type of nursing delivery unit or service (discontinue and replace)

02.40 Type of nursing delivery unit or serviceVselect all categories that most accurately describe the unit type or specialty (This is the National Database for Nursing Quality Indicators list in Appendix D http://www.nursingquality. org/Documents/Public/APPENDIX%20D.pdf). There are 2 levels of unit names, with the second unit level providing more distinct names. There are 36 higher-level-unit names with varying numbers of more specific unit names under higher-level names

2.40.01 Adult critical care unit 02.40.01.01 Adult burn critical care unit 02.40.01.02 Adult cardiothoracic critical care unit

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governmental and nursing quality improvement stan- dards and research. Now that the first 3 NMMDS data elements have been updated and are publicly available, these data elements can be used to support multilevel and multiagency analyses of the context of nursing care on patient safety, outcomes, and the nursing workforce information requirements. Given this, there are several implications for nurse manag- ers in use of the updated NMMDS data elements.

The NMMDS includes 18 essential data ele- ments; this study presents an update for the first 3 data elements. As identified in Table 1, implemen- tation of the NMMDS is useful to compare the im- pact of various nursing care delivery models or types and amount of staffing on workforce outcomes such as staff autonomy, retention, turnover, and satisfac- tion; the effective of changes such as implantation of EHRs on decision making and patient safety; or the impact of staff education, certification, and facility accreditation on patient outcomes and cost savings at the unit or service level. Imagine the information nurse managers and administrators would have at their fingertips if these variables were defined, coded, and routinely collected in a standardized manner for nurse managers and administrators to add to a data warehouse for comparison of clinical and workforce outcomes. For instance, studies demonstrated that certified and advanced practice nurses improve out- comes and reduce costs for specific patient popu- lations including mental healthcare,17 cardiac,18

neurological,19 and orthopedic conditions.20 How- ever, these studies are limited primarily to a small sample size and are costly to conduct. In 2009, the Wound, Ostomy, and Continence Nursing Society pro- vided a grant to Westra, Bliss, and Savik (2009) for $200,000 to evaluate the effect of certified wound, ostomy, and continence nurses on a national sample of approximately 1 million patients for outcomes of urinary and bowel incontinence, urinary tract infec- tions, and wounds including pressure ulcers, stasis ulcers, and surgical wound. This study reuses stan- dardized EHR and administrative data. If new data were collected with a conservative estimate of $1 per patient, the study would cost $1 million instead of $200,000. The cost of this study is possible only because home care agencies collect standardized assessment data and also track nurse visits with an associated staff ID, which can be linked with staff- ing characteristics such as certification. Reuse of standardized EHR clinical data along with nursing management data is critical to provide nurse man- agers with cost-effective information they need for management decisions.

Nurse managers, administrators, and researchers must advocate for inclusion and use of the NMMDS

Table 4. Comparison of the Previous and New Versions for NMMDS 03: Patient or Client Population

Previous NMMDS 03: Patient/Client Population

New NMMDS 03: Patient or Client Population

Characteristics of the population served by nursing delivery unit or service. Identify all categories that best describe the actual patient/client population served by the nursing delivery unit/service

03.1 Specialty (03.101-03.139)

RetiredVspecialty is redundant of new 02.03 type of nursing delivery unit or serviceExamples

03.101 AIDS/HIV 03.102 Birthing

03.2 Developmental focus 03.02 Chronological ageV percentage of the population during the reporting period of the appropriate age served on the nursing delivery unit or service (this is a modification of age categories listed at (http://www.seer.cancer.gov/ stdpopulations/stdpop. 19ages.html)

Examples

ExamplesVfetal, 9-28 d, then 5-year increments thereafter

03.201 Fetal

03.02.01 Fetal

03.202 Infant (aged 0-12 mo)

03.02.02 birth to 28 d

03.203 Toddler (aged 13-23 mo)

03.02.03 Aged 29 d to 1 y

03.204 Early childhood (aged 2-6 y)

03.02.04 Aged 1-4 y 03.02.05 Aged 5-9 y

03.3 Interaction focus Moved to NMMDS 04 03.31 Individual 03.32 Family 03.33 Group 03.34 Community/

population 03.4 Population focus 03.03 Catchment areaVthis is

an estimate of the percentage of patients served by this nursing delivery unit or service by geographical area. Select the smallest geographical unit that best fits the population served

03.41 City/town

03.03.01 Neighborhood

03.42 District

03.03.02 City or town

03.43 County/parish

03.03.03 District catchment area

03.44 Province

03.03.04 County catchment area

03.45 State

03.03.05 Parish catchment area

03.46 Region

03.03.06 State catchment area

03.47 Nation

03.03.07 Region catchment area

03.48 International

03.03.08 Nation catchment area

03.49 Aerospace

03.03.09 World catchment area 03.03.10 Aerospace

catchment area 03.03.11 Nautical

catchment area No previous subconcept 03.04 Total patient

populationVa count of the patient population during the reporting period

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data definitions and coding in health information systems. The NMMDS variables are a first step in standardizing nursing management data that can ex- plain variance in patient outcomes and factors influ- encing the nursing workforce. Practical steps include comparing the definitions for data elements reported in this article with existing data collected by the healthcare organization. Where data are compara- ble, no changes are required except to request that the data be abstracted and linked to clinical and bill- ing data in data warehouses. If the organizational data are not comparable, nurse managers need to reevaluate the way in which they define, capture, and store the data and request appropriate changes for future comparison of nursing management data across organizations.

Once nursing management data are standard- ized, stored, and linked in data warehouses, new reports can be requested to understand the relation- ship between nursing management data with inter- ventions and patient outcomes. Two of the quality indicators for meaningful use of EHRs include the percentage of patients receiving counseling for smok- ing cessation and of diabetics with adequate long- term glucose control.21 Nursing management data can extend an understanding of factors influencing compliance with these quality indicators by asking such questions as: Does an increase in compliance

with these quality indicators differ by the nursing unit? With the consistent definition and coding of nursing units, comparisons can be made across hospi- tals affiliated with the same health system or across health systems. Additional NMMDS variables (which are in the process of being updated) include certifi- cation of nurses, staff mix, and staff turnover. How do these variables influence compliance with quality indicators, and, hence, reimbursement from Medicare in the future?

Conclusion

The new criteria for meaningful use of EHRs will impact financial incentives beginning in October 2010 and disincentives beginning in 2015. Included in these criteria is reuse of EHR data for quality improvement. Nursing management data, in addi- tion to EHR data, are essential to explain variances in the quality of care. In this article, we described the process of updating the first 3 NMMDS data ele- ments. These data elements are now available publicly through the University of Minnesota School of Nursing Minimum Data Set Knowledge Discovery Web site and distributed beginning with release 2.24 of LOINC. We anticipate that this work will help organizations incorporate management data into their quality improvement programs.

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