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The American Journal of Managed Care Special Issue: Health Information Technology
Improving Adherence to Cardiovascular Disease Medications With Information Technology
William M. Vollmer, PhD; Ashli A. Owen-Smith, PhD; Jeffrey O. Tom, MD, MS; Reesa Laws, BS; Diane G. Ditmer, PharmD; David H. Smith, PhD; Amy C. Waterbury, MPH; Jennifer L. Schneider, MPH; Cyndee H. Yonehara, BS; Andrew Williams, PhD; Suma Vupputuri, PhD; and Cynthia S. Rand, PhD
Information Retrieval Pathways for Health Information Exchange in Multiple Care Settings
Patrick Kierkegaard, PhD; Rainu Kaushal, MD, MPH; and Joshua R. Vest, PhD, MPH
The 3 Key Themes in Health Information Technology
Julia Adler-Milstein, PhD
Leveraging EHRs to Improve Hospital Performance: The Role of Management
Julia Adler-Milstein, PhD; Kirstin Woody Scott, MPhil; and Ashish K. Jha, MD, MPH
Electronic Alerts and Clinician Turnover: The Influence of User Acceptance
Sylvia J. Hysong, PhD; Christiane Spitzmuller, PhD; Donna Espadas, BS; Dean F. Sittig, PhD; and Hardeep Singh, MD, MPH
Cost Implications of Human and Automated Follow-up in Ambulatory Care
Eta S. Berner, EdD; Jeffrey H. Burkhardt, PhD; Anantachai Panjamapirom, PhD; and Midge N. Ray, MSN, RN
Primary Care Capacity as Insurance Coverage Expands: Examining the Role of Health Information Technology
Renuka Tipirneni, MD, MSc; Ezinne G. Ndukwe, MPH; Melissa Riba, MS; HwaJung Choi, PhD; Regina Royan, MPH; Danielle Young, MPH; Marianne Udow-Phillips, MHSA; and Matthew M. Davis, MD, MAPP
Adoption of Electronic Prescribing for Controlled Substances Among Providers and Pharmacies
Meghan Hufstader Gabriel, PhD; Yi Yang, MD, PhD; Varun Vaidya, PhD; and Tricia Lee Wilkins, PharmD, PhD
Health Information Exchange and the Frequency of Repeat Medical Imaging
Joshua R. Vest, PhD, MPH; Rainu Kaushal, MD, MPH; Michael D. Silver, MS; Keith Hentel, MD, MS; and Lisa M. Kern, MD
Information Technology and Hospital Patient Safety: A Cross-Sectional Study of US Acute Care Hospitals
Ajit Appari, PhD; M. Eric Johnson, PhD; and Denise L. Anthony, PhD
Automated Detection of Retinal Disease
Lorens A. Helmchen, PhD; Harold P. Lehmann, MD, PhD; and Michael D. Abràmoff, MD, PhD
Currently Reading
Trending Health Information Technology Adoption Among New York Nursing Homes
Erika L. Abramson, MD, MS; Alison Edwards, MS; Michael Silver, MS; Rainu Kaushal, MD, MPH; and the HITEC investigators
The Value of Health Information Technology: Filling the Knowledge Gap
Robert S. Rudin, PhD; Spencer S. Jones, PhD; Paul Shekelle, MD, PhD; Richard J. Hillestad, PhD; and Emmett B. Keeler, PhD
Overcoming Barriers to a Research-Ready National Commercial Claims Database
David Newman, JD, PhD; Carolina-Nicole Herrera, MA; and Stephen T. Parente, PhD
The Effects of Health Information Technology Adoption and Hospital-Physician Integration on Hospital Efficiency
Na-Eun Cho, PhD; Jongwha Chang, PhD; and Bebonchu Atems, PhD

Trending Health Information Technology Adoption Among New York Nursing Homes

Erika L. Abramson, MD, MS; Alison Edwards, MS; Michael Silver, MS; Rainu Kaushal, MD, MPH; and the HITEC investigators
This study examines adoption of electronic health records and participation in health information exchange by New York state nursing homes over time.
Federal policies are incentivizing hospitals and providers to adopt and meaningfully use electronic health records (EHRs). Nursing homes are not eligible for incentives. However, understanding health information technology (HIT) adoption among nursing homes will be critical to developing HIT policies for this sector. Our objective was to assess the pace of EHR adoption, changes in computerized function adoption, and participation in health information exchange by New York state nursing homes over time.

Study Design
We used a repeated, cross-sectional study design.

We surveyed all New York state nursing homes between February and May 2013, comparing results to the same survey administered in 2012.

We received responses from 472 of 630 nursing homes (74.9%). Rates of EHR adoption increased from 48.6% to 56.3% (P = .03). Participation in health information exchange remained unchanged (54.5% to 55.3%, P = .8). The top barriers to EHR adoption cited were: a) the initial cost of HIT investment (67.9%, n = 133), b) lack of technical IT staff (46.4%, n = 91), and c) lack of fiscal incentives (45.8%, n = 88). Comparing nursing homes with EHRs in 2012 to nursing homes with EHRs in 2013, the availability of many types of computerized functionalities significantly increased, although no gains were seen for order entry or clinical tools.

While some gains are being made by nursing homes, HIT adoption generally lags behind that of other sectors. Public policy focusing on building HIT infrastructure is essential to ensure that nursing homes keep up with other healthcare segments.

Am J Manag Care. 2014;20(11 Spec No. 17):eSP53-eSP59
Although federal policies are incentivizing health information technology (HIT) adoption by hospitals and providers, nursing homes are excluded. Few large-scale or longitudinal assessments of HIT adoption in this sector have been published. We assessed electronic health record (EHR) adoption and health information exchange participation among New York state nursing homes over time.
  • There was a 7.7 percentage point increase in rates of EHR adoption between 2012 and 2013.
  • Rates of health information exchange remained stagnant.
  • HIT engagement in this sector lags behind other healthcare sectors.
  • Understanding HIT adoption in this sector is critical to guide policy development.
Recent federal policy has focused upon building the nation’s health information technology (HIT) infrastructure. Through the Electronic Health Record (EHR) Incentive Program, payments are available to hospitals and providers who meaningfully use EHRs.1 This program, with total incentive payments estimated between $14 billion and $27 billion, has resulted in tremendous increases in adoption in both sectors.2,3

Unlike hospitals and providers, the long-term care sector has been excluded from federal incentive programs. While only a few large-scale studies have assessed rates of EHR adoption among nursing homes—and reported rates vary widely—it is believed this sector lags behind.4-9 The barriers to adoption cited include the high cost of purchasing and maintaining EHRs, implementation and training challenges, and lack of evidence for return on investment.10 However, for those facilities that invest in EHRs, there are many reported benefits, including improved information access, more accurate documentation, increased adherence to evidence-based guidelines, and improved employee satisfaction and retention.11

Currently, there are more than 1.5 million residents in long-term care facilities—a number expected to increase as the elderly population grows.12 These patients are medically complex, have high medical costs, and are frequently transferred to acute care hospitals. Indeed, a 2011 report found that nursing homes transferred 25% of their Medicare residents annually to hospitals at a cost of $14.3 billion.13 As a result, this sector arguably has an urgent need to keep pace with the HIT adoption occurring in other sectors—a sentiment underscored by the Office of the National Coordinator for Health Information Technology.14

In 2012, we surveyed all New York state (NYS) nursing homes to assess their level of EHR adoption and health information exchange (HIE) participation.15 NYS leads the nation in state-based HIT investments, primarily through the Health Care Efficiency and Affordability Law for New Yorkers (HEAL NY) Capital Grants Program.16 While this program does not specifically provide funding to nursing homes, a primary focus is community-wide HIT investment.

The objective of this follow-up survey was to assess how rates of EHR adoption and HIE participation have changed over time. We also sought to identify characteristics associated with adoption during this time period, changes in computerized functions available, and barriers to EHR adoption. To our knowledge, this is the first such large-scale, repeated cross-sectional assessment. Given the rapidly changing HIT landscape, closely monitoring the progress of nursing homes can provide valuable data to inform HIT policies.


Survey Development

This study was conducted as part of the HEAL NY program evaluation process, led by researchers from HEAL NY’s designated evaluation entity, the Health Information Technology Evaluation Collaborative. Our survey instrument was a novel questionnaire developed with guidance from experts in HIT and HIE, as well as leaders from 3 key NYS long-term care associations: Continuing Care Leadership Coalition, LeadingAge New York, and New York State Health Facilities Association.17 Survey questions remained consistent between 2012 and 2013, allowing for longitudinal comparison. We obtained Institutional Review Board approval from Weill Cornell Medical College.

Survey Content

Our survey consisted of 10 questions and assessed EHR implementation, automation of 23 key functionalities, HIE participation, and barriers to EHR implementation.

Survey Sample and Administration

We surveyed all 630 NYS nursing homes between February and May 2013. Surveys were e-mailed to each nursing home administrator by Cornell Survey Research Institute, whose expertise is providing survey research support services. The 3 long-term care associations alerted administrators via electronic newsletters.

Nursing Home Characteristics

We collected information about nursing home location, bed size, hospital system membership, ownership, and status as a continuing care retirement community from the CMS Nursing Home Compare database.18 Continuing care retirement communities are generally privately owned and combine independent living, assisted living, and nursing home facilities. We speculated that their rates of EHR adoption and HIE participation may be higher than that of other nursing homes in order to better standardize care and facilitate exchange of information between facilities.

To categorize location, we used the catchment areas of the NYS Department of Health regional long-term care offices (Capital District, Central, Western, Hudson Valley, New York City, and Long Island).19 We divided bed size into 4 categories, with each representing approximately one-fourth of total nursing homes: less than 100, 100 to 159, 160 to 239, and 240 or more. We categorized ownership into privately owned for-profit, privately owned nonprofit, and publicly owned. Hospital affiliation, chain ownership, and status as a continuing care retirement community were dichotomized into yes/no variables.

Statistical Analysis

We compared respondent and nonrespondent characteristics using Pearson χ2 tests or Fisher’s exact tests. We evaluated whether an EHR was implemented either fully or partially (counting those facilities as adopters) and whether the facility participated in HIE. We used descriptive statistics to assess the availability of computerized functionalities among nursing homes with EHRs, comparing 2012 and 2013 results. For facilities participating in HIE, we assessed the type and directionality of data exchanged. We compared results between 2012 and 2013 using 2 sample tests of proportions.

We also analyzed the relationships between EHR adoption and HIE participation with key nursing home characteristics (eg, location, bed size, membership in a hospital system, ownership, and status as a continuing care retirement community), using logistic regression to isolate the predictive effects of each characteristic. We examined barriers to EHR adoption among nursing homes without EHRs, using χ2 or Fisher’s exact tests.

Lastly, we compared nursing homes that adopted EHRs between 2012 and 2013 to those that did not, and nursing homes that began participating in HIE between 2012 and 2013 to those that did not, in order to identify characteristics associated with HIT acquisition. We again used χ2 or Fisher’s exact tests to describe differences. We used SAS 9.3 (Cary, North Carolina) for all analyses.


We received responses from 472 of 630 nursing homes in 2013 (74.9%) (Table 1). There were no significant differences between respondents and nonrespondents.

Rates of EHR Adoption Over Time

There was a 7.7 percentage point increase in adoption between 2012 and 2013, from 48.6% to 56.3% (n = 264) (P = .03). In 2013, among the 119 nursing homes planning to implement EHRs, 22.7% (n = 27) planned to implement within 12 months, 37.8% (n = 45) within 13 to 24 months, 20.2% (n = 24) in 2 years or more, and 19.3% (n = 23) were unsure of the timeline. Only 11.7% (n = 55) reported no EHR implementation plans, similar to 2012 (11.4%).

Available Computerized Functionalities

As of 2013, among EHR adopters, the functionalities most likely to be all electronic were minimum data set reporting, financial management, and patient demographics (Table 2). Computerized provider order entry was available in 56.9% (n = 148) of facilities, and clinical notes were available in 51.4% (n = 133). Clinical decision support was only available in 8.2% (n = 21). Between 2012 and 2013, the availability of many types of computerized functionalities significantly increased among nursing homes with EHRs, particularly with regard to documentation and results viewing (Table 2). However, there was no significant change in the availability of medication or other type of order entry or clinical tool functions.

Participation in HIE

As of 2013, 50.5% (n = 233) of nursing homes exchanged key clinical information electronically within their care system and 27.2% (n = 125) exchanged information electronically outside their care system. Overall, the rate of HIE in 2013 was 55.3% (n = 256). There was no significant change in rates of HIE from 2012 to 2013 (54.5% to 55.3%, P = .8). Similar to 2012, the most common data exchange partners were pharmacies, hospitals, and laboratories (Table 3). Comparing facilities engaged in HIE in 2012 and 2013, there were no significant differences with regard to data exchange partners or the level of bidirectionality of data exchange.

Characteristics Associated With EHR Adoption and Participation in HIE

Only being part of a nursing home chain was significantly associated with having an EHR (odds ratio [OR] = 1.9; 95% CI, 1.0-3.4) (Appendix Table 1). Being affiliated with a hospital (OR = 2.4; 95% CI, 1.2-4.8), having an EHR (OR = 2.2; 95% CI, 1.5-3.2), and being a private nonprofit nursing home versus a for-profit nursing home (OR = 1.8; 95% CI, 1.3-2.7) were all significantly associated with HIE participation.

We also looked specifically at the 141 nursing homes that did not have EHRs in 2012 to identify any characteristics that distinguished the 2013 adopters (n = 45) from those who remained nonadopters (n = 96). We found no difference in facility characteristics between these 2 groups; however, among recent adopters we saw a significant increase in the use of many available computerized functionalities, a change we did not see among nonadopters. This was true for 16 of 23 functionalities, including 4 of 6 administrative functionalities, all documentation functionalities, all order entry functionalities, and consult viewing.

Similarly, we looked specifically at the 129 nursing homes that did not participate in HIE in 2012. We compared characteristics of those that participated in HIE in 2013 (n = 60) to nonparticipants both years (n = 69). We found that more of the facilities that newly engaged in HIE were medium-size (P = .02). In addition, there was significantly greater adoption in those facilities of several computerized functionalities—specifically medical history documentation (P = .04), allergy documentation (P = .04), and electronic laboratory results viewing (P = .01).

Barriers to EHR Adoption

The top barriers to EHR adoption among nursing homes without an EHR were stated to be: a) initial cost of HIT investment (67.9%, n = 133), b) lack of technical IT staff (46.4%, n = 91), and c) lack of fiscal incentives (45.8%, n = 88). Significantly more nursing homes without an EHR identified these as major barriers compared with those that had an EHR (P = .001, P <.0001, P = .03, respectively) (eAppendix Table 2).


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