J Med Syst (2015) 39:70 DOI 10.1007/s10916-015-0252-0
SYSTEMS-LEVEL QUALITY IMPROVEMENT
Use of Information Technology for Medication Management in Residential Care Facilities: Correlates of Facility Characteristics Soumitra S. Bhuyan 1 & Aastha Chandak 2 & M. Paige Powell 1 & Jungyoon Kim 2 & Olayinka Shiyanbola 3 & He Zhu 2 & Oyewale Shiyanbola 4
Received: 26 February 2015 / Accepted: 30 April 2015 # Springer Science+Business Media New York 2015
Abstract The effectiveness of information technology in resolving medication problems has been well documented. Long-term care settings such as residential care facilities (RCFs) may see the benefits of using such technologies in addressing the problem of medication errors among their resident population, who are usually older and have numerous chronic conditions. The aim of this study was two-fold: to examine the extent of use of Electronic Medication Management (EMM) in RCFs and to analyze the organizational factors associated with the use of EMM functionalities in RCFs. Data on RCFs were obtained from the 2010 National Survey of Residential Care Facilities. The association between facility, director and staff, and resident characteristics of RCFs and adoption of four EMM functionalities was assessed through multivariate logistic regression. The four EMM functionalities included were maintaining lists of medications, ordering for prescriptions, maintaining active medication allergy lists, and
This article is part of the Topical Collection on Systems-Level Quality Improvement * Soumitra S. Bhuyan [email protected] 1
Division of Health Systems Management and Policy, School of Public Health, The University of Memphis, 135 Robison Hall, Memphis, TN 38152, USA
2
Department of Health Services Research and Administration, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA
3
Social and Administrative Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI, USA
4
Population Health Science, University of Wisconsin, Madison, WI, USA
warning of drug interactions or contraindications. About 12 % of the RCFs adopted all four EMM functionalities. Additionally, maintaining lists of medications had the highest adoption rate (34.5 %), followed by maintaining active medication allergy lists (31.6 %), ordering for prescriptions (19.7 %), and warning of drug interactions or contraindications (17.9 %). Facility size and ownership status were significantly associated with adoption of all four EMM functionalities. Medicaid certification status, facility director’s age, education and license status, and the use of personal care aides in the RCF were significantly associated with the adoption of some of the EMM functionalities. EMM is expected to improve the quality of care and patient safety in long-term care facilities including RCFs. The extent of adoption of the four EMM functionalities is relatively low in RCFs. Some RCFs may strategize to use these functionalities to cater to the increasing demands from the market and also to provide better quality of care.
Keywords Long-term care . Residential care facility . Electronic medication management . Electronic health records
Abbreviations RCF Residential care facilities EHR Electronic health records EMM Electronic medication management ACA Patient Protection and Affordable Care Act ADE Adverse drug events NSRCF National survey of residential care facilities HPPD Direct care hours per patient per day PCA Personal care aide
70
J Med Syst (2015) 39:70
Page 2 of 8
Introduction Improper use of prescription medications among older adults is a serious public health concern in the United States (US) [1]. A larger percentage of older adults use significantly more prescription drugs than younger adults, making medication management more complicated and leaving older adults more susceptible to adverse drug events (ADEs) [2]. Improper medication-use includes over-use, under-use, incorrect dosage, non-adherence to prescribed medications, and use of potentially inappropriate medications as classified by the Beers list [3, 4]. Studies have shown that between 17 and 28 % of elderly adults have received potentially inappropriate medications [5, 6]. Medication mismanagement can result in several adverse health outcomes for older patients, along with higher medical costs [7, 8]. One study reported that such improper use of medications could also lead to adverse drug events (ADEs), which accounted for over 30 % of hospitalizations among the elderly population [9]. The Institute for Healthcare Informatics estimated over $200 billion in additional healthcare expenditures in the US in 2011 were attributable to incorrectly-used medications [10]. Among the elderly, this medication-use problem could be attributed to age-related barriers such as vision loss and cognitive impairment, confusion over doses and medication schedule as a result of complex treatment regimens, forgetfulness in taking medications, or cost-related non-adherence [11]. Overall, this can lead to a negative impact on a patient’s quality of life [12]. Residential care facilities (RCFs) provide services that fill the gap between home care and nursing homes [13]. These services include housing and assisting with daily activities such as personal hygiene, dressing, eating and taking medications [14]. In 2010, approximately one million Americans resided in RCFs in the US [15]. Numerous daily medications need to be given to residents in RCFs owing to their older age and prevalence of chronic conditions [14, 16]. Additionally, medication-related problems in RCFs such as adverse drug events (ADEs), mistreatment, and under-treatment have been documented in the past [17, 18]. These medication-related problems most frequently arise due to medication errors and ineffective management of medicines among residents [17]. The aging of the baby boomer population may lead to an increased demand for RCFs. With the burden of chronic illnesses in this older population leading to increasing use of medications, there is a need for appropriate medication management [14]. Information technology has been found to be effective in improving various aspects of quality of care including medication management [19–21]. Technologies such as Electronic Health Records (EHRs) and various computerized capabilities for medication management can improve medication management through enhanced communication, ease of
access to knowledge, knowing the right dose, ease of calculations, auditing and monitoring as well as providing decision support [22]. Although electronic medication management (EMM) through computerized systems seems to be a potential solution to address the problem of medication errors, various barriers to the implementation of such systems have been noted, including organizational structure, lack of resources, shortage of staff, inadequate training, and lack of financial incentives [23]. Unlike hospitals and physician offices, which are incentivized under the Patient Protection and Affordable Care Act (ACA) of 2010 to adopt EHRs and certain functionalities, long-term settings such as RCFs do not receive any financial incentives for adoption of EHRs [24]. Previous studies have examined the impact of organizational factors on the adoption and extent of the usage of computerized systems including EHRs, but few have focused on long-term care facilities like RCFs [25–31]. A recent report by the Centers for Disease Control and Prevention (CDC) highlighted that a relatively low percentage of RCFs (about 17 %) have adopted EHRs. Further, only about 40–70 % of them have adopted certain functionalities for medication management. However, this report only provides an overview of the status of adoption of computerized functionalities of those RCFs that adopted an EHR, but does not explore the extent of adoption of computerized capabilities for medication management in all RCFs, and also does not provide evidence for the characteristics of facilities that may be associated with such adoption [32]. Hence, using the first ever National Survey of Residential Care Facilities (2010), we examined the extent of adoption of EMM in RCFs. We also examined the organizational factors associated with the adoption of EMM in RCFs. This study specifically focuses on four types of EMM functionalities including maintaining lists of medications, ordering for prescriptions, maintaining active medication allergy lists, and warning of drug interactions or contraindications. Further, we also compared the adoption of EMM functionalities with the adoption of an EHR system in RCFs.
Methods Data sources Data on facility characteristics and EMM adoption were obtained from the National Survey of Residential Care Facilities (NSRCF), which was conducted by the CDC in 2010 [33]. The NSRCF is the first national probability sample survey for RCFs in the US which includes licensed, certified or registered assisted living residences, board and care homes, congregate care, enriched housing programs, homes for the aged, personal care homes, and shared housing establishments. This survey excludes facilities that are licensed to serve the
J Med Syst (2015) 39:70
severely mentally ill or developmentally disabled population and those that did not report having any current residents. The survey provides national weighting estimates that adjust for non-response, thereby enabling generalization of the sample to the all RCFs in the US. Both resident-level and facility-level information were collected: the facility-level data mainly includes characteristics of the organizations and services provided, and resident-level data includes demographic information of residents and their health status. This survey collected information on 2302 RCFs and 8094 residents. After applying weighting estimates for the national sample, we estimated results for 31,134 RCFs [33]. Measures The outcomes assessed in this study were four specific functionalities of using computerized capabilities for medication management in RCFs. These functionalities were: 1) maintaining lists of medications; 2) ordering for prescriptions; 3) maintaining active medication allergy lists; and 4) warning of drug interactions or contraindications. All four of these were coded as binary measures (yes or no) of whether the RCF adopted this EMM functionality or not. The predictor variables included in this study were grouped under facility, director and staff, and resident characteristics. Firstly, facility characteristics consist of facility size, chainaffiliation, ownership, and operation period. Facility size was categorized into small-sized (4–10 beds), medium-sized (11–25 beds), large-sized (26–100 beds), and extra-large-sized (100 beds and more). Chain-affiliation is a binary measure, which indicates whether the facility is owned by a ‘chain, group or multi-facility system’ or not. Ownership type was categorized into two types: ‘private for-profit’ and ‘private non-profit or state county or local government’. Further, operation period was categorized as RCFs operating for less than 10 years or RCFs operating for 10 years or more. Secondly, director- and staff-related factors include the director’s education level (categorized as less than college, some college, college graduate, and post graduate), age (categorized as 18– 39 years, 40–59 years, 60 years or older), whether the facility director is licensed to manage a facility for older people (yes or no), and the personal care aide (PCA) direct care hours per patient per day (HPPD) (categorized as 0 h, less than 1, 1– 1.999, 2–2.999, and 3 h or more) in the RCF. PCAs mainly include certified nursing assistants and medication technicians [33]. Finally, the percentage of residents ages 85 years and older and the percentage of white residents in the RCF were continuous variables representing resident characteristics. Statistical analysis The unit of analysis is the individual RCF. Descriptive statistics (Table 1) were used to calculate the weighted percentage
Page 3 of 8 70
of the four specific EMM functionalities (maintaining lists of medications, ordering for prescriptions, maintaining active medication allergy lists, and warning of drug interactions or contraindications). Descriptive statistics show the characteristics of all the RCFs together and of RCFs that adopted each of the four specific EMM functionalities. The survey weights in the NSRCF were used in calculating the weighted percentages to estimate the distribution of all RCFs in the US for each characteristic examined in this study. A weighted national sample of 31,134 facilities was included in this analysis. Multivariate logistic regression models were used to estimate the association between facility characteristics and the use of four EMM functionalities through four distinct models: Model 1 analyzed the adoption of maintaining lists of medications; Model 2 analyzed the adoption of ordering for prescriptions; Model 3 analyzed the adoption of maintaining active medication allergy lists; and Model 4 analyzed the adoption of warning of drug interactions or contraindications; and Model 5 analyzed the overall adoption of an EHR and not just EMM in RCFs. We compared the results from the first four models (EMM adoption results) with the results from Model 5 (EHR adoption results). All statistical analyses were conducted using SAS version 9.4 [34].
Results The adoption rates of EMM functionalities were relatively low, especially for ordering for prescriptions and warning of drug interactions or contraindications. Table 1 describes the characteristics of all RCFs and of only RCFs that adopted each of the four specific EMM functionalities considered in this study. Of 31,134 RCFs, 34.5 % of them adopted computer capacities to maintain lists of residents’ medication, 19.7 % adopted systems to electronically order prescriptions for residents, 31.6 % adopted systems to maintain active medication allergy lists, and 17.9 % adopted systems to warn about drug interactions or contraindications. Also, only 3,780 RCFs (12 %) had all four EMM functionalities at the same time. Among all the RCFs in the population sample, the majority were small-sized with 4 to 10 beds (49.6 %), had no chainaffiliation (62.3 %), private for-profit (82.4 %), and with an operating history of 10 years or more (56.2 %). Also, almost half of the RCFs had no Medicaid certification (50.2 %). In terms of director, staff and resident characteristics, 59.1 % of the directors were 40–50 years old, 44.3 % had graduated from college, and 83.4 % had licenses for managing a facility for older people. In 33.7 % of RCFs, PCAs worked 3 or more hours and 5.3 % of RCFs had no PCA. Further, among those RCFs who adopted any of the four EMM functionalities, a higher proportion had a chain-affiliation, a higher proportion were private non-profit or state, county or local government owned, a larger proportion had been operative 10 or more
70
J Med Syst (2015) 39:70
Page 4 of 8
Table 1
Descriptive statistics of all residential care facilities and the facilities that adopted the four electronic medication management functions, 2010
Measures
All facilities (N=31,134)
Maintaining lists of medications (N=10,741)
Ordering for prescriptions (N=6,133)
Maintaining active medication allergy lists (N=9,838)
Values
Weighted percentage (%)
Weighted percentage (%)
Weighted percentage (%)
Weighted percentage (%)
Warning of drug interactions or contraindications (N=5,573) Weighted percentage (%)
34.5
19.7
31.6
17.9
39.7
Adoption Facility factors Facility size Small (4–10 beds)
49.6
39.5
40.5
36.0
Medium (11–25 beds)
15.9
19.3
17.9
18.1
17.9
Large (26–100 beds)
27.8
32.1
31.3
35.2
30.9
Extra large (100 and more)
6.7
9.2
10.2
10.8
11.5
Chain-affiliation Yes
37.7
40.0
41.7
43.5
41.1
No
62.3
60.0
58.3
56.5
58.9
Private for profit
82.4
76.0
75.2
75.1
75.1
Private nonprofit or state, county or local government
17.6
24.0
24.8
24.9
24.9
Ownership type
Operation period =10 years
56.2
58.1
58.4
60.6
62.1
Medicaid certification Yes
49.8
52.8
51.5
52.7
52.5
No
50.2
47.2
48.5
47.3
47.5
18–39 years
18.6
16.0
17.0
16.8
18.2
40–59 years
59.1
61.6
59.0
62.0
59.3
60 years or older
22.3
22.4
24.0
21.2
22.6
Director and staff factors Director’s age
Director’s education Less than college
16.0
13.1
15.6
12.5
13.8
Some college
22.0
23.6
21.0
21.9
21.6
College graduate
44.3
40.7
40.7
43.8
42.6
Post-graduate
17.7
22.6
22.7
21.8
22.0
Director’s license Yes
83.4
83.8
87.9
85.1
86.3
No
16.6
16.2
12.1
14.9
13.7
0
5.3
2.9
2.7
2.7
2.2
Less than 1
16.0
16.5
16.5
17.8
16.9
1–1.999
26.5
28.7
27.5
30.9
29.0
2–2.999
18.5
19.2
20.0
18.4
19.3
3 and more
33.7
32.7
33.2
30.3
32.6
Mean
Mean
Mean
Mean
Mean
Percentage of residents aged 85 and older
48.8
50.1
49.4
51.1
48.9
Percentage of white residents
88.5
90.4
89.9
91.2
91.0
Personal care aide HPPD
Resident factors
Analyses adjusted for sample weights and complex survey design Source: 2010 National Survey of Residential Care Facilities
J Med Syst (2015) 39:70
years, and a larger proportion were certified by Medicaid as compared to all RCFs in the US. Additionally, among the RCFs that adopted any of the four EMM functionalities, a larger proportion had a director with post-graduate education as compared to all RCFs in the US. Table 2 presents the logistic regression results of the likelihood of adopting four specific functionalities for EMM. Facility size and ownership type were significantly associated with all four of the EMM functionalities, and Medicaid certification, director’s age, education and license status, and PCA HPPD were significantly associated with some of the EMM functionalities. Model 1 analyzed the adoption of EMM for maintaining lists of medications. The results from Model 1 show that, medium, large, and extra-large facilities were more likely to adopt EMM for maintaining lists of medications than smaller facilities (Medium vs. small OR, 1.79; 95 % CI: 1.37– 2.34; large vs. small OR, 1.73; 95 % CI, 1.28–2.34; extralarge vs. small OR, 2.37; 95 % CI, 1.59–3.55). Private nonprofit RCFs were 1.68 times more likely (OR, 1.68; 95 % CI, 1.33–2.11) to adopt EMM for maintaining lists of medications than private, for profit ones and Medicaid-certified facilities were 1.34 times more likely (OR, 1.34; 95 % CI, 1.08–1.66) to adopt EMM for maintaining lists of medications than noncertified facilities. Further, among the director and staff characteristics, RCFs with a facility director having a postgraduate education were 1.69 times more likely (OR, 1.69; 95 % CI, 1.14–2.50) to adopt EMM for maintaining lists of medications as compared to RCFs with a facility director having an education level of less than a college degree. Also, RCFs with a director aged 40 to 59 years were 1.34 times more likely to adopt EMM for maintaining medication lists (OR, 1.34; 95 % CI, 1.03–1.75) than RCFs with a director aged 18 to 39 years. RCFs with three or more PCA HPPD were 2.11 times more likely (OR, 2.11; 95 % CI, 1.09–4.11) to adopt EMM for maintaining lists of medications than RCFs with no PCA. Model 2 analyzed the adoption of EMM for ordering for prescriptions. According to the results from Model 2, medium, large and extra-large facilities were more likely to adopt EMM for ordering for prescriptions than smaller facilities (Medium vs. small OR, 1.46; 95 % CI, 1.05–2.03; large vs. small OR, 1.57; 95 % CI, 1.09–2.28; extra-large vs. small OR, 2.38; 95 % CI, 1.48–3.84). Private non-profit RCFs were 1.74 times more likely (OR, 1.74; 95 % CI, 1.34–2.27) to adopt EMM for ordering for prescriptions than private, for profit ones. Further, among the director and staff characteristics, RCFs with a licensed facility director were 1.74 times more likely (OR, 1.74; 95 % CI, 1.26–2.42) to adopt EMM for ordering for prescriptions than those that did not have a licensed director. Model 3 analyzed the adoption of EMM for maintaining active medication allergy lists. The results from Model 3 show that, medium, large and extra-large facilities were more likely to adopt EMM for maintaining active medication allergy lists
Page 5 of 8 70
than smaller facilities (Medium vs. small OR, 1.70; 95 % CI, 1.28–2.25; large vs. small OR, 1.99; 95 % CI, 1.46–2.73; extra-large vs. small OR, 3.02; 95 % CI, 2.00–4.55). Private non-profit RCFs were 1.69 times more likely (OR, 1.69; 95 % CI, 1.34–2.13) to adopt EMM for maintaining active medication allergy lists than private, for profit ones and Medicaidcertified facilities were 1.40 times more likely (OR, 1.40; 95 % CI, 1.12–1.75) to adopt EMM for maintaining active medication allergy lists than non-certified ones. Further, among the director and staff characteristics, RCFs with a licensed facility director were 1.31 times more likely (OR, 1.31; 95 % CI, 1.00–1.70) to adopt EMM for maintaining active medication allergy lists than those that did not have a licensed director. Model 4 analyzed the adoption of EMM for warning of drug interactions or contraindications. As seen from the results in Model 4, extra-large facilities were 2.47 times more likely to adopt EMM for warning of drug interactions or contraindications than smaller facilities (OR, 2.47; 95 % CI, 1.53–3.99). Private non-profit RCFs were 1.72 times more likely (OR, 1.72; 95 % CI, 1.31–2.25) to adopt EMM for warning of drug interactions or contraindications than private, for profit ones. Further, among the director and staff characteristics, RCFs with a licensed facility director were 1.45 times more likely (OR, 1.45; 95 % CI, 1.04–2.03) to adopt EMM warning of drug interactions or contraindications than those that did not have a licensed director. RCFs with three or more PCA HPPD were 2.7 times more likely (OR, 2.70; 95 % CI, 1.05–6.91) as compared to RCFs with no PCA to adopt EMM warning of drug interactions or contraindications. We also compared the adoption of these four specific EMM functionalities with the adoption of an EHR system by RCFs in Model 5. Similar to the first four models, facility characteristics such as increasing facility size, Medicaid certification, and private non-profit facility type were significantly associated with EHR adoption. Director- and staff-related factors and resident characteristics were not significantly associated with adoption of EHR. However, having a chain-affiliation had a significant association with adoption of EHR, although this association was not significant for the adoption of the four EMM functionalities.
Discussion This study examined the association between organizational factors of RCFs and use of four EMM functionalities in RCFs, using the 2010 NSRCF survey. Our study findings show an overall low rate of adoption of EMM in RCFs. Among a total of 31,134 RCFs, about 3,780 RCFs (12 %) adopted all four EMM functionalities. Out of the four EMM functionalities, maintaining lists of medications had the highest adoption rate (34.5 %), followed maintaining active medication allergy lists
70
J Med Syst (2015) 39:70
Page 6 of 8
Table 2 Multivariate logistic regression models for association between facility, director and staff, and resident characteristics and adoption of electronic medication management functionalities and overall EHR
Measures
Model 1
Model 2
Model 3
Model 4
Model 5
Maintain lists of medications
Ordering for prescriptions
Maintaining active medication allergy lists
Warning of drug interactions or contraindications
EHRs
OR
OR
CI
OR
CI
OR
CI
OR
CI
1.46* 1.57* 2.38*** 1.13 1.74***
[1.05,2.03] [1.09,2.28] [1.48,3.84] [0.88,1.45] [1.34,2.27]
1.70*** 1.99*** 3.02*** 1.10 1.69***
[1.28,2.25] [1.46,2.73] [2.00,4.55] [0.88,1.37] [1.34,2.13]
1.33 1.37 2.47*** 1.01 1.72***
[0.94,1.88] [0.93,2.00] [1.53,3.99] [0.78,1.31] [1.31,2.25]
1.08 1.63** 2.02** 1.40** 1.69***
[0.76,1.55] [1.13,2.34] [1.27,3.21] [1.09,1.79] [1.29,2.22]
1.15 0.91
[0.89,1.49] 1.40** [0.70,1.18] 1.01
[1.12,1.75] 1.22 [0.80,1.27] 1.13
[0.93,1.59] 1.60*** [1.24,2.05] [0.85,1.50] 1.09 [0.83,1.45]
[0.60,1.41] 1.24 [0.58,1.28] 1.21 [0.74,1.82] 1.49
[0.85,1.82] 1.12 [0.85,1.73] 1.09 [0.99,2.24] 1.33
[0.71,1.76] 1.26 [0.72,1.65] 1.24 [0.83,2.13] 1.31
[0.76,2.08] [0.78,1.97] [0.78,2.19]
[1.03,1.75] 1.15 [0.83,1.59] 1.29 [0.91,1.75] 1.27 [0.87,1.86] 1.12 [0.88,1.49] 1.74*** [1.26,2.42] 1.31*
[0.99,1.68] 1.05 [0.81,1.56] 1.06 [1.00,1.70] 1.45*
[0.76,1.45] 0.92 [0.71,1.58] 0.73 [1.04,2.03] 1.10
[0.67,1.25] [0.48,1.09] [0.81,1.50]
[0.78,3.10] [0.89,3.39] [0.99,3.83] [1.09,4.11]
[0.73,3.20] [0.81,3.40] [0.80,3.46] [0.91,3.81]
[0.69,4.96] [0.84,5.56] [0.93,6.36] [1.05,6.91]
2.40 2.00 2.33 2.37
[0.78,7.44] [0.65,6.15] [0.75,7.28] [0.77,7.30]
CI
Facility factors Facility size (Ref=Small) Medium 1.79*** [1.37,2.34] Large 1.73*** [1.28,2.34] Extra large 2.37*** [1.59,3.55] Chain-affiliation (Ref=No) 0.96 [0.77,1.18] Ownership type (Ref=private, 1.68*** [1.33,2.11] for profit) Medicaid certification (Ref=No) 1.34** [1.08,1.66] Operation period 0.90 [0.72,1.13] (Ref=
SYSTEMS-LEVEL QUALITY IMPROVEMENT
Use of Information Technology for Medication Management in Residential Care Facilities: Correlates of Facility Characteristics Soumitra S. Bhuyan 1 & Aastha Chandak 2 & M. Paige Powell 1 & Jungyoon Kim 2 & Olayinka Shiyanbola 3 & He Zhu 2 & Oyewale Shiyanbola 4
Received: 26 February 2015 / Accepted: 30 April 2015 # Springer Science+Business Media New York 2015
Abstract The effectiveness of information technology in resolving medication problems has been well documented. Long-term care settings such as residential care facilities (RCFs) may see the benefits of using such technologies in addressing the problem of medication errors among their resident population, who are usually older and have numerous chronic conditions. The aim of this study was two-fold: to examine the extent of use of Electronic Medication Management (EMM) in RCFs and to analyze the organizational factors associated with the use of EMM functionalities in RCFs. Data on RCFs were obtained from the 2010 National Survey of Residential Care Facilities. The association between facility, director and staff, and resident characteristics of RCFs and adoption of four EMM functionalities was assessed through multivariate logistic regression. The four EMM functionalities included were maintaining lists of medications, ordering for prescriptions, maintaining active medication allergy lists, and
This article is part of the Topical Collection on Systems-Level Quality Improvement * Soumitra S. Bhuyan [email protected] 1
Division of Health Systems Management and Policy, School of Public Health, The University of Memphis, 135 Robison Hall, Memphis, TN 38152, USA
2
Department of Health Services Research and Administration, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA
3
Social and Administrative Sciences Division, School of Pharmacy, University of Wisconsin, Madison, WI, USA
4
Population Health Science, University of Wisconsin, Madison, WI, USA
warning of drug interactions or contraindications. About 12 % of the RCFs adopted all four EMM functionalities. Additionally, maintaining lists of medications had the highest adoption rate (34.5 %), followed by maintaining active medication allergy lists (31.6 %), ordering for prescriptions (19.7 %), and warning of drug interactions or contraindications (17.9 %). Facility size and ownership status were significantly associated with adoption of all four EMM functionalities. Medicaid certification status, facility director’s age, education and license status, and the use of personal care aides in the RCF were significantly associated with the adoption of some of the EMM functionalities. EMM is expected to improve the quality of care and patient safety in long-term care facilities including RCFs. The extent of adoption of the four EMM functionalities is relatively low in RCFs. Some RCFs may strategize to use these functionalities to cater to the increasing demands from the market and also to provide better quality of care.
Keywords Long-term care . Residential care facility . Electronic medication management . Electronic health records
Abbreviations RCF Residential care facilities EHR Electronic health records EMM Electronic medication management ACA Patient Protection and Affordable Care Act ADE Adverse drug events NSRCF National survey of residential care facilities HPPD Direct care hours per patient per day PCA Personal care aide
70
J Med Syst (2015) 39:70
Page 2 of 8
Introduction Improper use of prescription medications among older adults is a serious public health concern in the United States (US) [1]. A larger percentage of older adults use significantly more prescription drugs than younger adults, making medication management more complicated and leaving older adults more susceptible to adverse drug events (ADEs) [2]. Improper medication-use includes over-use, under-use, incorrect dosage, non-adherence to prescribed medications, and use of potentially inappropriate medications as classified by the Beers list [3, 4]. Studies have shown that between 17 and 28 % of elderly adults have received potentially inappropriate medications [5, 6]. Medication mismanagement can result in several adverse health outcomes for older patients, along with higher medical costs [7, 8]. One study reported that such improper use of medications could also lead to adverse drug events (ADEs), which accounted for over 30 % of hospitalizations among the elderly population [9]. The Institute for Healthcare Informatics estimated over $200 billion in additional healthcare expenditures in the US in 2011 were attributable to incorrectly-used medications [10]. Among the elderly, this medication-use problem could be attributed to age-related barriers such as vision loss and cognitive impairment, confusion over doses and medication schedule as a result of complex treatment regimens, forgetfulness in taking medications, or cost-related non-adherence [11]. Overall, this can lead to a negative impact on a patient’s quality of life [12]. Residential care facilities (RCFs) provide services that fill the gap between home care and nursing homes [13]. These services include housing and assisting with daily activities such as personal hygiene, dressing, eating and taking medications [14]. In 2010, approximately one million Americans resided in RCFs in the US [15]. Numerous daily medications need to be given to residents in RCFs owing to their older age and prevalence of chronic conditions [14, 16]. Additionally, medication-related problems in RCFs such as adverse drug events (ADEs), mistreatment, and under-treatment have been documented in the past [17, 18]. These medication-related problems most frequently arise due to medication errors and ineffective management of medicines among residents [17]. The aging of the baby boomer population may lead to an increased demand for RCFs. With the burden of chronic illnesses in this older population leading to increasing use of medications, there is a need for appropriate medication management [14]. Information technology has been found to be effective in improving various aspects of quality of care including medication management [19–21]. Technologies such as Electronic Health Records (EHRs) and various computerized capabilities for medication management can improve medication management through enhanced communication, ease of
access to knowledge, knowing the right dose, ease of calculations, auditing and monitoring as well as providing decision support [22]. Although electronic medication management (EMM) through computerized systems seems to be a potential solution to address the problem of medication errors, various barriers to the implementation of such systems have been noted, including organizational structure, lack of resources, shortage of staff, inadequate training, and lack of financial incentives [23]. Unlike hospitals and physician offices, which are incentivized under the Patient Protection and Affordable Care Act (ACA) of 2010 to adopt EHRs and certain functionalities, long-term settings such as RCFs do not receive any financial incentives for adoption of EHRs [24]. Previous studies have examined the impact of organizational factors on the adoption and extent of the usage of computerized systems including EHRs, but few have focused on long-term care facilities like RCFs [25–31]. A recent report by the Centers for Disease Control and Prevention (CDC) highlighted that a relatively low percentage of RCFs (about 17 %) have adopted EHRs. Further, only about 40–70 % of them have adopted certain functionalities for medication management. However, this report only provides an overview of the status of adoption of computerized functionalities of those RCFs that adopted an EHR, but does not explore the extent of adoption of computerized capabilities for medication management in all RCFs, and also does not provide evidence for the characteristics of facilities that may be associated with such adoption [32]. Hence, using the first ever National Survey of Residential Care Facilities (2010), we examined the extent of adoption of EMM in RCFs. We also examined the organizational factors associated with the adoption of EMM in RCFs. This study specifically focuses on four types of EMM functionalities including maintaining lists of medications, ordering for prescriptions, maintaining active medication allergy lists, and warning of drug interactions or contraindications. Further, we also compared the adoption of EMM functionalities with the adoption of an EHR system in RCFs.
Methods Data sources Data on facility characteristics and EMM adoption were obtained from the National Survey of Residential Care Facilities (NSRCF), which was conducted by the CDC in 2010 [33]. The NSRCF is the first national probability sample survey for RCFs in the US which includes licensed, certified or registered assisted living residences, board and care homes, congregate care, enriched housing programs, homes for the aged, personal care homes, and shared housing establishments. This survey excludes facilities that are licensed to serve the
J Med Syst (2015) 39:70
severely mentally ill or developmentally disabled population and those that did not report having any current residents. The survey provides national weighting estimates that adjust for non-response, thereby enabling generalization of the sample to the all RCFs in the US. Both resident-level and facility-level information were collected: the facility-level data mainly includes characteristics of the organizations and services provided, and resident-level data includes demographic information of residents and their health status. This survey collected information on 2302 RCFs and 8094 residents. After applying weighting estimates for the national sample, we estimated results for 31,134 RCFs [33]. Measures The outcomes assessed in this study were four specific functionalities of using computerized capabilities for medication management in RCFs. These functionalities were: 1) maintaining lists of medications; 2) ordering for prescriptions; 3) maintaining active medication allergy lists; and 4) warning of drug interactions or contraindications. All four of these were coded as binary measures (yes or no) of whether the RCF adopted this EMM functionality or not. The predictor variables included in this study were grouped under facility, director and staff, and resident characteristics. Firstly, facility characteristics consist of facility size, chainaffiliation, ownership, and operation period. Facility size was categorized into small-sized (4–10 beds), medium-sized (11–25 beds), large-sized (26–100 beds), and extra-large-sized (100 beds and more). Chain-affiliation is a binary measure, which indicates whether the facility is owned by a ‘chain, group or multi-facility system’ or not. Ownership type was categorized into two types: ‘private for-profit’ and ‘private non-profit or state county or local government’. Further, operation period was categorized as RCFs operating for less than 10 years or RCFs operating for 10 years or more. Secondly, director- and staff-related factors include the director’s education level (categorized as less than college, some college, college graduate, and post graduate), age (categorized as 18– 39 years, 40–59 years, 60 years or older), whether the facility director is licensed to manage a facility for older people (yes or no), and the personal care aide (PCA) direct care hours per patient per day (HPPD) (categorized as 0 h, less than 1, 1– 1.999, 2–2.999, and 3 h or more) in the RCF. PCAs mainly include certified nursing assistants and medication technicians [33]. Finally, the percentage of residents ages 85 years and older and the percentage of white residents in the RCF were continuous variables representing resident characteristics. Statistical analysis The unit of analysis is the individual RCF. Descriptive statistics (Table 1) were used to calculate the weighted percentage
Page 3 of 8 70
of the four specific EMM functionalities (maintaining lists of medications, ordering for prescriptions, maintaining active medication allergy lists, and warning of drug interactions or contraindications). Descriptive statistics show the characteristics of all the RCFs together and of RCFs that adopted each of the four specific EMM functionalities. The survey weights in the NSRCF were used in calculating the weighted percentages to estimate the distribution of all RCFs in the US for each characteristic examined in this study. A weighted national sample of 31,134 facilities was included in this analysis. Multivariate logistic regression models were used to estimate the association between facility characteristics and the use of four EMM functionalities through four distinct models: Model 1 analyzed the adoption of maintaining lists of medications; Model 2 analyzed the adoption of ordering for prescriptions; Model 3 analyzed the adoption of maintaining active medication allergy lists; and Model 4 analyzed the adoption of warning of drug interactions or contraindications; and Model 5 analyzed the overall adoption of an EHR and not just EMM in RCFs. We compared the results from the first four models (EMM adoption results) with the results from Model 5 (EHR adoption results). All statistical analyses were conducted using SAS version 9.4 [34].
Results The adoption rates of EMM functionalities were relatively low, especially for ordering for prescriptions and warning of drug interactions or contraindications. Table 1 describes the characteristics of all RCFs and of only RCFs that adopted each of the four specific EMM functionalities considered in this study. Of 31,134 RCFs, 34.5 % of them adopted computer capacities to maintain lists of residents’ medication, 19.7 % adopted systems to electronically order prescriptions for residents, 31.6 % adopted systems to maintain active medication allergy lists, and 17.9 % adopted systems to warn about drug interactions or contraindications. Also, only 3,780 RCFs (12 %) had all four EMM functionalities at the same time. Among all the RCFs in the population sample, the majority were small-sized with 4 to 10 beds (49.6 %), had no chainaffiliation (62.3 %), private for-profit (82.4 %), and with an operating history of 10 years or more (56.2 %). Also, almost half of the RCFs had no Medicaid certification (50.2 %). In terms of director, staff and resident characteristics, 59.1 % of the directors were 40–50 years old, 44.3 % had graduated from college, and 83.4 % had licenses for managing a facility for older people. In 33.7 % of RCFs, PCAs worked 3 or more hours and 5.3 % of RCFs had no PCA. Further, among those RCFs who adopted any of the four EMM functionalities, a higher proportion had a chain-affiliation, a higher proportion were private non-profit or state, county or local government owned, a larger proportion had been operative 10 or more
70
J Med Syst (2015) 39:70
Page 4 of 8
Table 1
Descriptive statistics of all residential care facilities and the facilities that adopted the four electronic medication management functions, 2010
Measures
All facilities (N=31,134)
Maintaining lists of medications (N=10,741)
Ordering for prescriptions (N=6,133)
Maintaining active medication allergy lists (N=9,838)
Values
Weighted percentage (%)
Weighted percentage (%)
Weighted percentage (%)
Weighted percentage (%)
Warning of drug interactions or contraindications (N=5,573) Weighted percentage (%)
34.5
19.7
31.6
17.9
39.7
Adoption Facility factors Facility size Small (4–10 beds)
49.6
39.5
40.5
36.0
Medium (11–25 beds)
15.9
19.3
17.9
18.1
17.9
Large (26–100 beds)
27.8
32.1
31.3
35.2
30.9
Extra large (100 and more)
6.7
9.2
10.2
10.8
11.5
Chain-affiliation Yes
37.7
40.0
41.7
43.5
41.1
No
62.3
60.0
58.3
56.5
58.9
Private for profit
82.4
76.0
75.2
75.1
75.1
Private nonprofit or state, county or local government
17.6
24.0
24.8
24.9
24.9
Ownership type
Operation period =10 years
56.2
58.1
58.4
60.6
62.1
Medicaid certification Yes
49.8
52.8
51.5
52.7
52.5
No
50.2
47.2
48.5
47.3
47.5
18–39 years
18.6
16.0
17.0
16.8
18.2
40–59 years
59.1
61.6
59.0
62.0
59.3
60 years or older
22.3
22.4
24.0
21.2
22.6
Director and staff factors Director’s age
Director’s education Less than college
16.0
13.1
15.6
12.5
13.8
Some college
22.0
23.6
21.0
21.9
21.6
College graduate
44.3
40.7
40.7
43.8
42.6
Post-graduate
17.7
22.6
22.7
21.8
22.0
Director’s license Yes
83.4
83.8
87.9
85.1
86.3
No
16.6
16.2
12.1
14.9
13.7
0
5.3
2.9
2.7
2.7
2.2
Less than 1
16.0
16.5
16.5
17.8
16.9
1–1.999
26.5
28.7
27.5
30.9
29.0
2–2.999
18.5
19.2
20.0
18.4
19.3
3 and more
33.7
32.7
33.2
30.3
32.6
Mean
Mean
Mean
Mean
Mean
Percentage of residents aged 85 and older
48.8
50.1
49.4
51.1
48.9
Percentage of white residents
88.5
90.4
89.9
91.2
91.0
Personal care aide HPPD
Resident factors
Analyses adjusted for sample weights and complex survey design Source: 2010 National Survey of Residential Care Facilities
J Med Syst (2015) 39:70
years, and a larger proportion were certified by Medicaid as compared to all RCFs in the US. Additionally, among the RCFs that adopted any of the four EMM functionalities, a larger proportion had a director with post-graduate education as compared to all RCFs in the US. Table 2 presents the logistic regression results of the likelihood of adopting four specific functionalities for EMM. Facility size and ownership type were significantly associated with all four of the EMM functionalities, and Medicaid certification, director’s age, education and license status, and PCA HPPD were significantly associated with some of the EMM functionalities. Model 1 analyzed the adoption of EMM for maintaining lists of medications. The results from Model 1 show that, medium, large, and extra-large facilities were more likely to adopt EMM for maintaining lists of medications than smaller facilities (Medium vs. small OR, 1.79; 95 % CI: 1.37– 2.34; large vs. small OR, 1.73; 95 % CI, 1.28–2.34; extralarge vs. small OR, 2.37; 95 % CI, 1.59–3.55). Private nonprofit RCFs were 1.68 times more likely (OR, 1.68; 95 % CI, 1.33–2.11) to adopt EMM for maintaining lists of medications than private, for profit ones and Medicaid-certified facilities were 1.34 times more likely (OR, 1.34; 95 % CI, 1.08–1.66) to adopt EMM for maintaining lists of medications than noncertified facilities. Further, among the director and staff characteristics, RCFs with a facility director having a postgraduate education were 1.69 times more likely (OR, 1.69; 95 % CI, 1.14–2.50) to adopt EMM for maintaining lists of medications as compared to RCFs with a facility director having an education level of less than a college degree. Also, RCFs with a director aged 40 to 59 years were 1.34 times more likely to adopt EMM for maintaining medication lists (OR, 1.34; 95 % CI, 1.03–1.75) than RCFs with a director aged 18 to 39 years. RCFs with three or more PCA HPPD were 2.11 times more likely (OR, 2.11; 95 % CI, 1.09–4.11) to adopt EMM for maintaining lists of medications than RCFs with no PCA. Model 2 analyzed the adoption of EMM for ordering for prescriptions. According to the results from Model 2, medium, large and extra-large facilities were more likely to adopt EMM for ordering for prescriptions than smaller facilities (Medium vs. small OR, 1.46; 95 % CI, 1.05–2.03; large vs. small OR, 1.57; 95 % CI, 1.09–2.28; extra-large vs. small OR, 2.38; 95 % CI, 1.48–3.84). Private non-profit RCFs were 1.74 times more likely (OR, 1.74; 95 % CI, 1.34–2.27) to adopt EMM for ordering for prescriptions than private, for profit ones. Further, among the director and staff characteristics, RCFs with a licensed facility director were 1.74 times more likely (OR, 1.74; 95 % CI, 1.26–2.42) to adopt EMM for ordering for prescriptions than those that did not have a licensed director. Model 3 analyzed the adoption of EMM for maintaining active medication allergy lists. The results from Model 3 show that, medium, large and extra-large facilities were more likely to adopt EMM for maintaining active medication allergy lists
Page 5 of 8 70
than smaller facilities (Medium vs. small OR, 1.70; 95 % CI, 1.28–2.25; large vs. small OR, 1.99; 95 % CI, 1.46–2.73; extra-large vs. small OR, 3.02; 95 % CI, 2.00–4.55). Private non-profit RCFs were 1.69 times more likely (OR, 1.69; 95 % CI, 1.34–2.13) to adopt EMM for maintaining active medication allergy lists than private, for profit ones and Medicaidcertified facilities were 1.40 times more likely (OR, 1.40; 95 % CI, 1.12–1.75) to adopt EMM for maintaining active medication allergy lists than non-certified ones. Further, among the director and staff characteristics, RCFs with a licensed facility director were 1.31 times more likely (OR, 1.31; 95 % CI, 1.00–1.70) to adopt EMM for maintaining active medication allergy lists than those that did not have a licensed director. Model 4 analyzed the adoption of EMM for warning of drug interactions or contraindications. As seen from the results in Model 4, extra-large facilities were 2.47 times more likely to adopt EMM for warning of drug interactions or contraindications than smaller facilities (OR, 2.47; 95 % CI, 1.53–3.99). Private non-profit RCFs were 1.72 times more likely (OR, 1.72; 95 % CI, 1.31–2.25) to adopt EMM for warning of drug interactions or contraindications than private, for profit ones. Further, among the director and staff characteristics, RCFs with a licensed facility director were 1.45 times more likely (OR, 1.45; 95 % CI, 1.04–2.03) to adopt EMM warning of drug interactions or contraindications than those that did not have a licensed director. RCFs with three or more PCA HPPD were 2.7 times more likely (OR, 2.70; 95 % CI, 1.05–6.91) as compared to RCFs with no PCA to adopt EMM warning of drug interactions or contraindications. We also compared the adoption of these four specific EMM functionalities with the adoption of an EHR system by RCFs in Model 5. Similar to the first four models, facility characteristics such as increasing facility size, Medicaid certification, and private non-profit facility type were significantly associated with EHR adoption. Director- and staff-related factors and resident characteristics were not significantly associated with adoption of EHR. However, having a chain-affiliation had a significant association with adoption of EHR, although this association was not significant for the adoption of the four EMM functionalities.
Discussion This study examined the association between organizational factors of RCFs and use of four EMM functionalities in RCFs, using the 2010 NSRCF survey. Our study findings show an overall low rate of adoption of EMM in RCFs. Among a total of 31,134 RCFs, about 3,780 RCFs (12 %) adopted all four EMM functionalities. Out of the four EMM functionalities, maintaining lists of medications had the highest adoption rate (34.5 %), followed maintaining active medication allergy lists
70
J Med Syst (2015) 39:70
Page 6 of 8
Table 2 Multivariate logistic regression models for association between facility, director and staff, and resident characteristics and adoption of electronic medication management functionalities and overall EHR
Measures
Model 1
Model 2
Model 3
Model 4
Model 5
Maintain lists of medications
Ordering for prescriptions
Maintaining active medication allergy lists
Warning of drug interactions or contraindications
EHRs
OR
OR
CI
OR
CI
OR
CI
OR
CI
1.46* 1.57* 2.38*** 1.13 1.74***
[1.05,2.03] [1.09,2.28] [1.48,3.84] [0.88,1.45] [1.34,2.27]
1.70*** 1.99*** 3.02*** 1.10 1.69***
[1.28,2.25] [1.46,2.73] [2.00,4.55] [0.88,1.37] [1.34,2.13]
1.33 1.37 2.47*** 1.01 1.72***
[0.94,1.88] [0.93,2.00] [1.53,3.99] [0.78,1.31] [1.31,2.25]
1.08 1.63** 2.02** 1.40** 1.69***
[0.76,1.55] [1.13,2.34] [1.27,3.21] [1.09,1.79] [1.29,2.22]
1.15 0.91
[0.89,1.49] 1.40** [0.70,1.18] 1.01
[1.12,1.75] 1.22 [0.80,1.27] 1.13
[0.93,1.59] 1.60*** [1.24,2.05] [0.85,1.50] 1.09 [0.83,1.45]
[0.60,1.41] 1.24 [0.58,1.28] 1.21 [0.74,1.82] 1.49
[0.85,1.82] 1.12 [0.85,1.73] 1.09 [0.99,2.24] 1.33
[0.71,1.76] 1.26 [0.72,1.65] 1.24 [0.83,2.13] 1.31
[0.76,2.08] [0.78,1.97] [0.78,2.19]
[1.03,1.75] 1.15 [0.83,1.59] 1.29 [0.91,1.75] 1.27 [0.87,1.86] 1.12 [0.88,1.49] 1.74*** [1.26,2.42] 1.31*
[0.99,1.68] 1.05 [0.81,1.56] 1.06 [1.00,1.70] 1.45*
[0.76,1.45] 0.92 [0.71,1.58] 0.73 [1.04,2.03] 1.10
[0.67,1.25] [0.48,1.09] [0.81,1.50]
[0.78,3.10] [0.89,3.39] [0.99,3.83] [1.09,4.11]
[0.73,3.20] [0.81,3.40] [0.80,3.46] [0.91,3.81]
[0.69,4.96] [0.84,5.56] [0.93,6.36] [1.05,6.91]
2.40 2.00 2.33 2.37
[0.78,7.44] [0.65,6.15] [0.75,7.28] [0.77,7.30]
CI
Facility factors Facility size (Ref=Small) Medium 1.79*** [1.37,2.34] Large 1.73*** [1.28,2.34] Extra large 2.37*** [1.59,3.55] Chain-affiliation (Ref=No) 0.96 [0.77,1.18] Ownership type (Ref=private, 1.68*** [1.33,2.11] for profit) Medicaid certification (Ref=No) 1.34** [1.08,1.66] Operation period 0.90 [0.72,1.13] (Ref=
