54009
lied GerontologyHolup et al.
JAG33410.1177/0733464812454009
Brief Reports
Going Digital: Adoption of Electronic Health Records in Assisted Living Facilities
Journal of Applied Gerontology 2014,Vol. 33(4) 494–504 © The Author(s) 2012 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0733464812454009 jag.sagepub.com
Amanda A. Holup1, Debra Dobbs1, April Temple2, and Kathryn Hyer1
Abstract This pilot study examines the associations between structural characteristics and the adoption and subsequent use of electronic health records (EHR; resident demographics, clinical notes, medication lists, problem lists, discharge summaries, and advance directives) as a process characteristic in assisted living facilities (ALFs).The study is guided conceptually by Donabedian’s Structure-Process-Outcome (SPO) model. Primary survey data were collected from a randomly selected sample (N = 76) in Florida during 2009-2010. Analysis included descriptive and bivariate statistics. Descriptive results indicated that ALFs most frequently used an EHR to record medication lists. Characteristics, including size, profit status, resident case mix, and staffing, were associated at the bivariate level with the use of one or more functional domains of an EHR. Thus, the use of EHRs in ALFs is correlated with facility characteristics. Keywords electronic health record, assisted living facility, health information technology
Manuscript received: January 3, 2012; final revision received: April 4, 2012; accepted: May 28, 2012. 1
University of South Florida, Tampa, FL, USA James Madison University, Harrisonburg,VA, USA
2
Corresponding Author: Amanda A. Holup, University of South Florida, 13301 Bruce B Downs BLVD, MHC 1316A, Tampa, FL 33612, USA. Email: [email protected]
Downloaded from jag.sagepub.com at NORTHERN ILLINOIS UNIV on March 25, 2015
495
Holup et al.
Introduction The 2009 federal Health Information Technology for Economic and Clinical Health (HITECH) Act allocated US$27 billion to doctors and hospitals to promote the use of electronic health records (EHR). Although EHRs have the potential to improve the quality and safety of health care, eliminate inefficiencies, reduce costs, and encourage greater patient engagement (Kramer, Richard, Epstein, Winn, & May, 2009; Shekelle, Morton, & Keeler, 2006), adopting meaningful use of this technology has proven difficult (Black et al., 2011). Currently, qualified health centers, rural clinics, hospitals, and physicians’ offices are eligible to receive Medicare and Medicaid incentives to adopt EHRs; however, long-term care providers (nursing homes, assisted living facilities [ALFs], home health, rehabilitation centers, and adult day care) are ineligible. Despite the growing numbers of older adults who require coordination of care across multiple long-term care settings, the adoption of EHRs is not as ubiquitous within these domains as compared to acute care settings (Ferris, 2005). Prior studies examining EHR use in nursing homes (NH) suggest that those facilities that are members of a chain, as well as larger, nonprofit facilities with a greater number of services and longer tenure of the administrator, were more likely to use EHRs for clinical care and administrative domains (Chan, 2008; Davis, Brannon, & Whitman, 2009; Resnick, Manard, Stone, & Alwan, 2009). Across home health and hospice agencies, nonprofit or government providers, providers that were members of a chain, and those with a larger patient census were more likely to use EHRs (Bercovitz, Sengupta, & Jamison, 2010). One growing long-term care setting that has not been studied in regards to EHR utilization is ALFs. Approximately 1 million older adults reside in 31,100 ALFs nationwide (Park-Lee et al., 2011) and that number is expected to reach 1.9 million by 2030 (Mollica, Sims-Kastelein, & O’Keefe, 2008). Since the past decade, ALFs have evolved into a viable alternative to NH placement (Ball et al., 2000; Chapin & Dobbs-Kepper, 2001; Spillman & McGillard, 2002) as most ALF residents tend to be frail older adults with multiple comorbidities and deteriorating physical health (Kane & Mach, 2007; Street, Burge, & Quadagno, 2009). Although ALF residents are similar to NH residents with respect to depressive symptoms, physical impairments, behavioral problems, and changes in morbidity (Sloane et al., 2004), ALFs are not health care facilities, which leaves residents vulnerable to hospitalizations (Becker, Boaz, Andel, & DeMuth, 2011) and medications mismanagement (Briesacher, Limcangco, Simoni-Wastila, Doshi, & Gurwitz, 2005; Sloane et al., 2004). Consequently, EHR integration into ALFs may improve residents’ health outcomes and reduce unnecessary health care expenditures.
Downloaded from jag.sagepub.com at NORTHERN ILLINOIS UNIV on March 25, 2015
496
Journal of Applied Gerontology 33(4)
Conceptual Framework The Structure-Process-Outcome (SPO) model (Donabedian, 1996) has been widely used to conceptualize and study quality of care in long-term care. According to this model, quality is composed of three related domains: structure (characteristics of the physical/organizational setting), process (the technical processes of care), and resident outcomes (Donabedian, Wheeler, & Wyszewianski, 1982). Donabedian’s model provides a framework to examine how structure and process of care have the potential to cause injury or harm to residents.
Purpose Using the SPO theoretical model, this pilot study examines the associations between structural characteristics and the use of EHRs in ALFs. To our knowledge, this is the first study to provide insight into the use of EHRs in ALFs.
Method Sample The sample (n = 741) was selected from the 2,768 Florida licensed ALFs and 490 adult family care homes (AFCH) in 2009. AFCHs were also included because they represent residential care settings in Florida that provide very similar long-term care services as ALFs and are only differentiated in licensure by their bed capacity. Florida is an ideal site for examining ALFs since it has a diverse industry and accounts for 33% of facilities nationwide (Mollica, Johnson-LaMarche, & O’Keefe, 2005). The sample was stratified based on three facility-size categories (high: >15 beds, medium: 7-15 beds, and low: 3 ADLS Medicaid primary payer Private primary payer License type Extended congregate care (ECC) Limited mental health (LMH) For profit, publicly traded
52.3 (42.9) 3.4 (5.8) 11.8 (14.2) 77.7 79.4 55.9 27.1 38.9 52.9 26.0 72.3 29.3 9.3 13.3
Results Descriptive statistics for the final sample are presented in Table 1. On average, the facilities had 52 beds and staffed slightly more than 3 nursing FTE (RN/ LPN) and 11 PCA FTEs, respectively. Approximately 13% were for profit. Twenty-nine percent had a license for extended congregate care and only 9% were licensed as limited mental health. The majority of residents were female (77%), Caucasian (79%), and private pay (72%). About half of residents had dementia and significant ADL limitations. Approximately 53.9% of ALFs surveyed reported using at least one or more of the EHR domains. A comparison between those using EHRs (n = 41) and those not using the technology (n = 35) suggested that users differ significantly from nonusers based on profit status, as users were more likely to be for-profit, publicly traded facilities (χ2 = 6.22, p = .013). Among these facilities using EHRs (n = 41), there was considerable variation in the reported use of each EHR component (Figure 1). Of those facilities with EHR capabilities, the most commonly reported functions were the ability to record medication lists (87.8%) and the documentation of resident demographics (73.2%), whereas EHR use for discharge summaries (39%) and advance directives (34.1%) was less common. Correlation analysis indicated that several ALF characteristics, including size, profit status, resident case mix factors, and staffing, were significantly associated Downloaded from jag.sagepub.com at NORTHERN ILLINOIS UNIV on March 25, 2015
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Holup et al.
Figure 1. The use of specific electronic health record (EHR) domains in assisted living facilities currently using EHRs (n = 41).aThese percentages include those facilities that used at least one of the six EHR domains.
with the subsequent use of EHR to record resident demographics, clinical notes, medication lists, and resident problems (Table 2). Using an EHR to document resident demographics was correlated with larger, for-profit facilities that employed a higher number of PCAs, RNs, and/or LPNs. These facilities also had a higher percentage of Caucasian and female residents as well as residents with a diagnosis of dementia. EHRs used to record clinical notes were positively correlated with larger, for-profit facilities that had a higher percentage of female and Caucasian residents. Status as a for-profit, publicly traded facility was correlated with the use of EHRs to document resident problems and medication. No facility characteristics were correlated with the use of EHRs for discharge summaries or advance directives.
Discussion The findings of this pilot study provide initial evidence that ALFs are currently utilizing EHRs and serve as a preliminary step toward analyzing EHR use in ALFs. Based on these findings, the most commonly reported functions of EHRs are the ability to record medication lists and resident demographics. Although, considerable variability exists in the proportion of ALFs using EHR components, our prevalence findings are consistent with research examining these trends in home health and hospice agencies (Resnick & Alwan, 2010). Although the finding that status as a for-profit, publicly traded facility is correlated with EHR use for several domains did not follow the direction suggested by existing research in NHs (Davis et al., 2009), several studies have
Downloaded from jag.sagepub.com at NORTHERN ILLINOIS UNIV on March 25, 2015
500
Downloaded from jag.sagepub.com at NORTHERN ILLINOIS UNIV on March 25, 2015
.30** .145 .192 .243* .301** .25 .04 .09 .25* .06 .01 .473***
.325** .384*** .32* .07 .10 .28* .13 .02 .40***
Clinical notes
.36*** .23* .25*
Resident demographics
.109 .10 –.03 –.13 .06 .05 .03 .13 .33**
.13 .04 –.02
Problem lists
.186 .15 .13 .08 .06 .09 .03 .06 .26*
.17 .12 .15
Medication lists
.009 –.04 –.03 –.15 .13 –.08 .09 .17 .18
.02 .04 –.05
Discharge summaries
–.016 –.05 –.03 –.12 .08 –.17 –.01 .08 .22
.04 .02 –.06
Advance directives
Note: N = 76. FTE = full-time equivalent; RN = registered nurses; LPN = licensed practical nurses; PCA = personal care aides; ADL = activities of daily living. *p < .05. **p < .01. ***p < .001.
Total beds FTE RN & LPN FTE personal care aides Resident case mix Female Caucasian Diagnosis of dementia Assist
lied GerontologyHolup et al.
JAG33410.1177/0733464812454009
Brief Reports
Going Digital: Adoption of Electronic Health Records in Assisted Living Facilities
Journal of Applied Gerontology 2014,Vol. 33(4) 494–504 © The Author(s) 2012 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0733464812454009 jag.sagepub.com
Amanda A. Holup1, Debra Dobbs1, April Temple2, and Kathryn Hyer1
Abstract This pilot study examines the associations between structural characteristics and the adoption and subsequent use of electronic health records (EHR; resident demographics, clinical notes, medication lists, problem lists, discharge summaries, and advance directives) as a process characteristic in assisted living facilities (ALFs).The study is guided conceptually by Donabedian’s Structure-Process-Outcome (SPO) model. Primary survey data were collected from a randomly selected sample (N = 76) in Florida during 2009-2010. Analysis included descriptive and bivariate statistics. Descriptive results indicated that ALFs most frequently used an EHR to record medication lists. Characteristics, including size, profit status, resident case mix, and staffing, were associated at the bivariate level with the use of one or more functional domains of an EHR. Thus, the use of EHRs in ALFs is correlated with facility characteristics. Keywords electronic health record, assisted living facility, health information technology
Manuscript received: January 3, 2012; final revision received: April 4, 2012; accepted: May 28, 2012. 1
University of South Florida, Tampa, FL, USA James Madison University, Harrisonburg,VA, USA
2
Corresponding Author: Amanda A. Holup, University of South Florida, 13301 Bruce B Downs BLVD, MHC 1316A, Tampa, FL 33612, USA. Email: [email protected]
Downloaded from jag.sagepub.com at NORTHERN ILLINOIS UNIV on March 25, 2015
495
Holup et al.
Introduction The 2009 federal Health Information Technology for Economic and Clinical Health (HITECH) Act allocated US$27 billion to doctors and hospitals to promote the use of electronic health records (EHR). Although EHRs have the potential to improve the quality and safety of health care, eliminate inefficiencies, reduce costs, and encourage greater patient engagement (Kramer, Richard, Epstein, Winn, & May, 2009; Shekelle, Morton, & Keeler, 2006), adopting meaningful use of this technology has proven difficult (Black et al., 2011). Currently, qualified health centers, rural clinics, hospitals, and physicians’ offices are eligible to receive Medicare and Medicaid incentives to adopt EHRs; however, long-term care providers (nursing homes, assisted living facilities [ALFs], home health, rehabilitation centers, and adult day care) are ineligible. Despite the growing numbers of older adults who require coordination of care across multiple long-term care settings, the adoption of EHRs is not as ubiquitous within these domains as compared to acute care settings (Ferris, 2005). Prior studies examining EHR use in nursing homes (NH) suggest that those facilities that are members of a chain, as well as larger, nonprofit facilities with a greater number of services and longer tenure of the administrator, were more likely to use EHRs for clinical care and administrative domains (Chan, 2008; Davis, Brannon, & Whitman, 2009; Resnick, Manard, Stone, & Alwan, 2009). Across home health and hospice agencies, nonprofit or government providers, providers that were members of a chain, and those with a larger patient census were more likely to use EHRs (Bercovitz, Sengupta, & Jamison, 2010). One growing long-term care setting that has not been studied in regards to EHR utilization is ALFs. Approximately 1 million older adults reside in 31,100 ALFs nationwide (Park-Lee et al., 2011) and that number is expected to reach 1.9 million by 2030 (Mollica, Sims-Kastelein, & O’Keefe, 2008). Since the past decade, ALFs have evolved into a viable alternative to NH placement (Ball et al., 2000; Chapin & Dobbs-Kepper, 2001; Spillman & McGillard, 2002) as most ALF residents tend to be frail older adults with multiple comorbidities and deteriorating physical health (Kane & Mach, 2007; Street, Burge, & Quadagno, 2009). Although ALF residents are similar to NH residents with respect to depressive symptoms, physical impairments, behavioral problems, and changes in morbidity (Sloane et al., 2004), ALFs are not health care facilities, which leaves residents vulnerable to hospitalizations (Becker, Boaz, Andel, & DeMuth, 2011) and medications mismanagement (Briesacher, Limcangco, Simoni-Wastila, Doshi, & Gurwitz, 2005; Sloane et al., 2004). Consequently, EHR integration into ALFs may improve residents’ health outcomes and reduce unnecessary health care expenditures.
Downloaded from jag.sagepub.com at NORTHERN ILLINOIS UNIV on March 25, 2015
496
Journal of Applied Gerontology 33(4)
Conceptual Framework The Structure-Process-Outcome (SPO) model (Donabedian, 1996) has been widely used to conceptualize and study quality of care in long-term care. According to this model, quality is composed of three related domains: structure (characteristics of the physical/organizational setting), process (the technical processes of care), and resident outcomes (Donabedian, Wheeler, & Wyszewianski, 1982). Donabedian’s model provides a framework to examine how structure and process of care have the potential to cause injury or harm to residents.
Purpose Using the SPO theoretical model, this pilot study examines the associations between structural characteristics and the use of EHRs in ALFs. To our knowledge, this is the first study to provide insight into the use of EHRs in ALFs.
Method Sample The sample (n = 741) was selected from the 2,768 Florida licensed ALFs and 490 adult family care homes (AFCH) in 2009. AFCHs were also included because they represent residential care settings in Florida that provide very similar long-term care services as ALFs and are only differentiated in licensure by their bed capacity. Florida is an ideal site for examining ALFs since it has a diverse industry and accounts for 33% of facilities nationwide (Mollica, Johnson-LaMarche, & O’Keefe, 2005). The sample was stratified based on three facility-size categories (high: >15 beds, medium: 7-15 beds, and low: 3 ADLS Medicaid primary payer Private primary payer License type Extended congregate care (ECC) Limited mental health (LMH) For profit, publicly traded
52.3 (42.9) 3.4 (5.8) 11.8 (14.2) 77.7 79.4 55.9 27.1 38.9 52.9 26.0 72.3 29.3 9.3 13.3
Results Descriptive statistics for the final sample are presented in Table 1. On average, the facilities had 52 beds and staffed slightly more than 3 nursing FTE (RN/ LPN) and 11 PCA FTEs, respectively. Approximately 13% were for profit. Twenty-nine percent had a license for extended congregate care and only 9% were licensed as limited mental health. The majority of residents were female (77%), Caucasian (79%), and private pay (72%). About half of residents had dementia and significant ADL limitations. Approximately 53.9% of ALFs surveyed reported using at least one or more of the EHR domains. A comparison between those using EHRs (n = 41) and those not using the technology (n = 35) suggested that users differ significantly from nonusers based on profit status, as users were more likely to be for-profit, publicly traded facilities (χ2 = 6.22, p = .013). Among these facilities using EHRs (n = 41), there was considerable variation in the reported use of each EHR component (Figure 1). Of those facilities with EHR capabilities, the most commonly reported functions were the ability to record medication lists (87.8%) and the documentation of resident demographics (73.2%), whereas EHR use for discharge summaries (39%) and advance directives (34.1%) was less common. Correlation analysis indicated that several ALF characteristics, including size, profit status, resident case mix factors, and staffing, were significantly associated Downloaded from jag.sagepub.com at NORTHERN ILLINOIS UNIV on March 25, 2015
499
Holup et al.
Figure 1. The use of specific electronic health record (EHR) domains in assisted living facilities currently using EHRs (n = 41).aThese percentages include those facilities that used at least one of the six EHR domains.
with the subsequent use of EHR to record resident demographics, clinical notes, medication lists, and resident problems (Table 2). Using an EHR to document resident demographics was correlated with larger, for-profit facilities that employed a higher number of PCAs, RNs, and/or LPNs. These facilities also had a higher percentage of Caucasian and female residents as well as residents with a diagnosis of dementia. EHRs used to record clinical notes were positively correlated with larger, for-profit facilities that had a higher percentage of female and Caucasian residents. Status as a for-profit, publicly traded facility was correlated with the use of EHRs to document resident problems and medication. No facility characteristics were correlated with the use of EHRs for discharge summaries or advance directives.
Discussion The findings of this pilot study provide initial evidence that ALFs are currently utilizing EHRs and serve as a preliminary step toward analyzing EHR use in ALFs. Based on these findings, the most commonly reported functions of EHRs are the ability to record medication lists and resident demographics. Although, considerable variability exists in the proportion of ALFs using EHR components, our prevalence findings are consistent with research examining these trends in home health and hospice agencies (Resnick & Alwan, 2010). Although the finding that status as a for-profit, publicly traded facility is correlated with EHR use for several domains did not follow the direction suggested by existing research in NHs (Davis et al., 2009), several studies have
Downloaded from jag.sagepub.com at NORTHERN ILLINOIS UNIV on March 25, 2015
500
Downloaded from jag.sagepub.com at NORTHERN ILLINOIS UNIV on March 25, 2015
.30** .145 .192 .243* .301** .25 .04 .09 .25* .06 .01 .473***
.325** .384*** .32* .07 .10 .28* .13 .02 .40***
Clinical notes
.36*** .23* .25*
Resident demographics
.109 .10 –.03 –.13 .06 .05 .03 .13 .33**
.13 .04 –.02
Problem lists
.186 .15 .13 .08 .06 .09 .03 .06 .26*
.17 .12 .15
Medication lists
.009 –.04 –.03 –.15 .13 –.08 .09 .17 .18
.02 .04 –.05
Discharge summaries
–.016 –.05 –.03 –.12 .08 –.17 –.01 .08 .22
.04 .02 –.06
Advance directives
Note: N = 76. FTE = full-time equivalent; RN = registered nurses; LPN = licensed practical nurses; PCA = personal care aides; ADL = activities of daily living. *p < .05. **p < .01. ***p < .001.
Total beds FTE RN & LPN FTE personal care aides Resident case mix Female Caucasian Diagnosis of dementia Assist
