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There are 15,632 nursing homes (NHs) in the United States. NHs continue to receive significant policy attention due to high costs and poor outcomes of care. One strategy for improving NH care is use of health information technology (HIT). A central concept of this study is HIT maturity, which is used to identify adoption trends in HIT capabilities, use and integration within resident care, clinical support, and administrative activities. This concept is guided by the Nolan stage theory, which postulates that a system such as HIT moves through a series of measurable stages. HIT maturity is an important component of the rapidly changing NH landscape, which is being affected by policies generated to protect residents, in part because of the pandemic.
The aim of this study is to identify structural disparities in NH HIT maturity and see if it is moderated by commonly used organizational characteristics.
NHs (n=6123, >20%) were randomly recruited from each state using Nursing Home Compare data. Investigators used a validated HIT maturity survey with 9 subscales including HIT capabilities, extent of HIT use, and degree of HIT integration in resident care, clinical support, and administrative activities. Each subscale had a possible HIT maturity score of 0-100. Total HIT maturity, with a possible score of 0-900, was calculated using the 9 subscales (3 x 3 matrix). Total HIT maturity scores equate 1 of 7 HIT maturity stages (stages 0-6) for each facility. Dependent variables included HIT maturity scores. We included 5 independent variables (ie, ownership, chain status, location, number of beds, and occupancy rates). Unadjusted and adjusted cumulative odds ratios were calculated using regression models.
Our sample (n=719) had a larger proportion of smaller facilities and a smaller proportion of larger facilities than the national nursing home population. Integrated clinical support technology had the lowest HIT maturity score compared to resident care HIT capabilities. The majority (n=486, 60.7%) of NHs report stage 3 or lower with limited capabilities to communicate about care delivery outside their facility. Larger NHs in metropolitan areas had higher odds of HIT maturity. The number of certified beds and NH location were significantly associated with HIT maturity stage while ownership, chain status, and occupancy rate were not.
NH structural disparities were recognized through differences in HIT maturity stage. Structural disparities in this sample appear most evident in HIT maturity, measuring integration of clinical support technologies for laboratory, pharmacy, and radiology services. Ongoing assessments of NH structural disparities is crucial given 1.35 million Americans receive care in these facilities annually. Leaders must be willing to promote equal opportunities across the spectrum of health care services to incentivize and enhance HIT adoption to balance structural disparities and improve resident outcomes.
There are 15,632 nursing homes (NHs) in the United States with 1.7 million beds and 1.3 million Americans residing in them [
In this paper, we define HIT as a system that is used in health care to process, store, and exchange health information in an electronic environment. The use of HIT in NHs has also been recognized by experts in the field as a method to improve NH quality [
A central concept of this study is HIT maturity guided by stage theory by Nolan [
HIT maturity is an important component of the rapidly changing NH landscape. In previous work, we have found alternating patterns of total HIT maturity over 3 years (2014-2017) among 815 NHs; that is, (n=579, 71%) of NHs exhibited net positive increase in total HIT maturity, (n=155, 19%) had a net negative decrease in total HIT maturity, and (n=58, 10%) had consistently negative patterns of total HIT maturity over time [
The development of HIT maturity surveys has allowed researchers to begin exploring the relationship between technology use and NH resident level outcomes. For instance, recent studies have revealed mixed associations between HIT maturity and antibiotic use and urinary tract infections. In one study, linking HIT maturity data with a resident-level minimum data set yielded 219,461 regular resident assessments within 90 days of survey completion on 80,237 unique, older adult long-stay residents. We found that for every 10-point increase in the HIT maturity score, the expected odds of antibiotic use increased by 7% [
The COVID-19 pandemic has also influenced changes in HIT use [
This research was conducted as part of a larger ongoing 3-year national study (2019-2022) exploring trends in NH HIT maturity in the United States. Data were collected in 2019 using an NH survey that measures 3 dimensions of HIT maturity (ie, HIT capabilities, use, and integration) in the 3 domains of resident care, clinical support (ie, laboratory, pharmacy, and radiology), and administrative activities. All methods used in this research were approved by the Columbia University Institutional Review Board (PT-AABR3810).
Nursing home compare is a publicly available database, maintained by the Centers for Medicare & Medicaid Services, which provides information about every US NH serving beneficiaries of Medicare or Medicaid [
The sample recruitment goal for this study was 10% of all NHs in the United States (N=1570 NH). Based on our previous experience with a 45% response rate, more than 20% (n=6123) of the facilities were randomly recruited from each state. The number of facilities selected in each state was proportional to the number of NHs located in that state. For example, because California has the largest number of homes (n=1241), 248 homes (20% of facilities) were randomly selected from all California NHs. To ensure that responses were received from multiple NHs in each state, we overrecruited in states with smaller numbers to have a minimum of 6 homes per state. Although sampling was stratified by state, facilities were not stratified further by the number of certified beds, ownership, location, and so on prior to recruitment; this is because some states may not have any NHs in some strata. Wyoming, for example, has only 38 homes. In Wyoming, there are fewer large homes in rural areas. By including every NH in the random selection process within each state, every home in each state—regardless of their characteristics—had an equal opportunity to participate.
Comparison of national nursing home population vs sample.
Nursing home characteristics | National (N=14,109) | Sample (n=719) | Probability ratio or Cohen |
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.07 | |||||||||
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Metro >50,000 | 9823 (69.68) | 453 (63) | 1.11 |
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Micro 10,000-49,999 | 1936 (13.73) | 114 (15.86) | 0.87 |
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Small town 2500-9999 | 1414 (10.03) | 90 (12.52) | 0.80 |
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Rural <2500 | 925 (6.56) | 62 (8.62) | 0.76 |
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<.001 | |||||||||
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<60 | 2418 (17.14) | 150 (20.86) | 0.82 |
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60-120 | 7582 (53.74) | 420 (58.41) | 0.92 |
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>120 | 4109 (29.12) | 149 (20.72) | 1.41 |
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.89 | |||||||||
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Nonprofit | 3903 (27.66) | 193 (26.84) | 1.03 |
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For-profit | 10,206 (72.34) | 526 (73.16) | 0.99 |
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.43 | |||||||||
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Yes | 10,627 (75.32) | 551 (76.63) | 0.98 |
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No | 3482 (24.68) | 168 (23.37) | 1.06 |
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Occupancy rate, mean (SD) | 0.812 (0.2) | 0.806 (0.15) |
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.46 |
aCalculated as the probabilities of national data divided by the sample data for categorical variable or Cohen
Our psychometrically sound NH HIT survey has 9 subscales, from which 7 HIT maturity stages are derived [
Five NH characteristics were included. Ownership type was collapsed into the 2 categories of “For Profit” and “Nonprofit” (nonprofit included NHs with a government classification in nursing home compare). A binary chain status variable was created. Rural-Urban Commuting Area Codes were used to classify the NHs by ZIP Codes into 4 regional locations including the following: metropolitan >50,000; micropolitan 10,000-49,999; small town 2500-9999; and rural <2500 [
Probability ratios were computed to compare NH characteristics between the national data and the study sample [
A total of 719 homes completed the survey with all 50 states and the District of Columbia being represented. The comparison of the national population of eligible NHs and the study sample is provided in
The aggregated raw HIT maturity scores are shown as Table S1 in
The weighted HIT maturity scores accounting for number of responses by state are illustrated in Table S2 in
Nursing Home HIT Maturity Stages and Definitions. EHR: electronic health record; HIT: health information technology.
NHs at stage 3 have internal connectivity and reporting capabilities, meaning that these staff have limited capacity to communicate about care delivery with people outside their facility, such as with staff from external clinics, laboratories, or pharmacies. To be able to communicate electronically with people outside their facilities, NHs must reach a stage 4 or higher, and 32.4% (n=233) reached a stage 4 or higher. Nearly 3% (20/719 NHs) have achieved stage 6, the highest possible stage. In these facilities, data use by residents or residents’ representatives are available to generate clinical data and to drive self-management activities.
The results of the ordinal logistic regression models (
Simple ordinal logistic regression model assessing the relationship between nursing home characteristics and health information technology maturity stage (n=719).
Nursing home characteristics | Unadjusted cumulative odds ratio | 95% CI | |||
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60-12 | 0.93 | 0.64 (1.35) | .71 | |
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<60 | 0.39 | 0.25 (0.63) | <.001a | |
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Micro | 0.66 | 0.42 (1.02) | .06 | |
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Rural | 0.36 | 0.23 (0.58) | <.001a | |
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Small town | 0.53 | 0.31 (0.91) | .02a | |
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Nonprofit vs for-profit | 0.80 | 0.58 (1.10) | .16 | |
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Chain vs nonchain | 1.09 | 0.77 (1.56) | .63 | |
Occupancy rate | 2.21 | 0.83 (5.90) | .11 |
a
Multivariable ordinal logistic regression model assessing the relationship between nursing home characteristics and HIT maturity stage (n=719).
Nursing home characteristics | Adjusted cumulative odds ratio | 95% CI | |||||
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60-12 | 0.99 | 0.67 (1.46) | .96 | |||
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<60 | 0.45 | 0.28 (0.73) | .001a | |||
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Micro | 0.65 | 0.41 (1.03) | .07 | |||
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Rural | 0.45 | 0.29 (0.71) | .001a | |||
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Small town | 0.57 | 0.33 (1.00) | .045a | |||
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Nonprofit vs for-profit | 0.84 | 0.61 (1.17) | .30 | |||
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Chain vs nonchain | 1.12 | 0.78 (1.61) | .54 | |||
Occupancy rate | 1.71 | 0.63 (4.66) | .30 |
a
The results from this study indicate that structural disparities in HIT maturity exist. Most facilities, nearly 68% (n=486) report being at stage 3 or lower of HIT maturity indicating they are not able to electronically communicate externally with other facilities. This lack of connectivity can result in reduced levels of electronic data sharing, leading to deficiencies in care delivery, substandard care coordination activities, and poorer resident outcomes [
Ongoing assessments and characterization of structural disparities in NHs is crucial given 1.35 million Americans receive care in these facilities annually [
Clearly, when incentives are provided or barriers are removed from HIT adoption, facilities will respond in ways that reduce structural disparities and promote better care delivery. To some extent, current incentive programs through meaningful use appear to be influencing HIT adoption in NHs and information-sharing practices with other clinical settings such as hospitals [
Our survey uses broad constructs to describe structural disparities in this sample of NHs. However, we have used rigorous methods to be sure our measures have been informed by highly experienced and qualified members of the NH community [
In this national sample, we identified important structural disparities in NHs that are likely impacting the quality of care their residents are receiving. The majority of these NHs have lower HIT maturity levels, reporting a gap in connectivity with external facilities that might otherwise enhance health data sharing across different organizations. These differences could be due to inadequate infrastructures, availability of a knowledgeable workforce, or financial resources to promote higher levels of adoption. It is crucial that we begin to consistently identify a means to address these disparities, first by increasing transparency and public reporting about the trends in NH HIT maturity in the United States, followed by implementing national policies to level these deficits.
Increasingly, at the forefront of policies affecting NH care delivery is the awareness that structural disparities can place undue burden on practicing NH leaders and staff to provide high-quality care to residents. However, underneath this problem is a lack of structured and standardized means to identify and report existing structural disparities in NHs in the United States. In the absence of systematic reporting mechanisms to identify existing structural disparities in NHs, these issues will go undetected, and leaders, staff, and residents will continue to suffer the consequences.
Table S1.
Table S2.
Table S3.
electronic health records
health information technology
nursing home
odds ratio
This project was supported by grant number R01HS022497 from the Agency for Healthcare Research and Quality. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. The authors wish to thank our Project Coordinator (Keely Wise) and Recruitment Team (Brooke Schrimpf, Joy Bennett, Chris Duren, and Laurie Janeczko) who have worked diligently toward our goal of improving care of older adults in nursing homes.
The data sets generated and analyzed during this study are not publicly available due to the project not being completed but are available from the corresponding author upon reasonable request following completion of the project.
None declared.