Journal of Gerontological Nursing

Technology Innovations Supplemental Data

Exploring Resident Care Information Technology Use and Nursing Home Quality

Kimberly R. Powell, PhD, RN; Chelsea B. Deroche, PhD; Ethan J. Carnahan; Gregory L. Alexander, PhD, RN, FAAN, FACMI

Abstract

A wide array of sophisticated information technology (IT) systems are being used in nursing home (NH) resident care to improve quality. The purpose of the current study was to explore differences in NH IT sophistication, a comprehensive measure of adoption, used in resident care processes based on facility characteristics over 4 consecutive years and to examine the impact on select long-stay NH quality measures. Results indicate IT systems used in resident care are becoming increasingly sophisticated. NH bed size, type of ownership, and location were significant predictors of IT score in areas related to resident care. Results also suggest that as electronic clinical processes and documents increase (e.g., incident reporting, nursing flowsheets, care planning) in resident care, more falls with injury are detected. Continued assessments of NH IT sophistication are important as the impact of technology on quality continues to be evaluated. [Journal of Gerontological Nursing, 46(4), 15–20.]

Abstract

A wide array of sophisticated information technology (IT) systems are being used in nursing home (NH) resident care to improve quality. The purpose of the current study was to explore differences in NH IT sophistication, a comprehensive measure of adoption, used in resident care processes based on facility characteristics over 4 consecutive years and to examine the impact on select long-stay NH quality measures. Results indicate IT systems used in resident care are becoming increasingly sophisticated. NH bed size, type of ownership, and location were significant predictors of IT score in areas related to resident care. Results also suggest that as electronic clinical processes and documents increase (e.g., incident reporting, nursing flowsheets, care planning) in resident care, more falls with injury are detected. Continued assessments of NH IT sophistication are important as the impact of technology on quality continues to be evaluated. [Journal of Gerontological Nursing, 46(4), 15–20.]

Health information technology (IT) systems are designed to assist in the delivery, support, and management of patient care. Although implementation of IT systems has grown dramatically in recent years in acute and ambulatory care, other sectors, such as nursing homes (NHs), have experienced slower uptake. For example, in 2017, 88% of hospitals were able to send electronic patient information to other providers; in contrast, a recent national study found only 46% of NHs have any capability to exchange health information electronically (American Hospital Association, 2019; Powell et al., 2020). Although adoption of IT systems was not federally incentivized in NHs, there is increasing pressure to implement such systems. Payment changes, including a shift from fee-for-service to prospective, value-based payment, and market-based pressures are escalating the need for sophisticated IT systems to increase efficiencies and improve quality and safety (Grabowski et al., 2017; Huckfeldt et al., 2018). As NHs take heed of the political and consumer-driven demand for improved IT capabilities, it is important to understand trends in adoption and how NH IT impacts quality.

A wide array of IT systems can be used in providing care to NH residents, which could impact quality. These systems are used in direct resident care by different members of the interdisciplinary team, including nurses, nurse aides, physical therapists, occupational therapists, and others. For example, IT systems can be used to digitize documents such as medication administration records, care plans, nursing flowsheets, and incident reports. Although these technologies are being used in some NHs, a systematic assessment of NH IT sophistication is necessary to trend adoption and begin to understand how these systems influence quality of care. Currently, there are no reported links between use of IT systems and quality reported on a national level.

Members of the current authors' research team have been measuring IT adoption in NHs, also called IT sophistication, since 2006 (Alexander et al., 2008). A survey measuring IT sophistication across three health care domains (i.e., resident care, clinical support, and administrative activities) and three IT dimensions (i.e., IT capabilities, extent of use, and degree of integration) has been developed and tested to create a complete and systematic assessment of NH IT. The relationship between nationally reported NH quality measures (QMs) and the nine dimensions/domains of IT sophistication have begun to be examined, but evaluation of these dimensions/domains within specific content areas (CAs) and their impact on quality is just beginning (Alexander & Madsen, 2018). Each of the nine dimensions and domains are broken down into 27 CAs with associated content items used to describe and trend a full range of IT sophistication measures (Alexander et al., 2020).

By examining relationships between specific CAs and QMs, work continues toward the overall goal of determining which technologies make a difference in NH quality to inform practice and policy. Relating specific technologies to quality requires a deeper level of granularity than what has previously been explored. The aims of the current study were: (1) to explore differences in NH IT sophistication used in resident care processes based on facility characteristics (e.g., bed size, location, type of ownership) over 4 consecutive years, and (2) to examine the impact NH IT sophistication in resident care processes has on select QMs. A guiding model of independent variables (i.e., NH characteristics, IT sophistication CAs, time in years) and dependent variables (i.e., NH QMs) is presented in Figure 1.

Conceptual model to test nursing home (NH) information technology (IT) sophistication and quality measures.Note. PT = physical therapy; OT = occupational therapy; PC = personal computer.

Figure 1.

Conceptual model to test nursing home (NH) information technology (IT) sophistication and quality measures.

Note. PT = physical therapy; OT = occupational therapy; PC = personal computer.

Method

The current research includes a longitudinal design using surveys of U.S. NHs that were randomly selected from each state using the publicly available Nursing Home Compare dataset. NHs were recruited over a 4-year period from January 1, 2014 to January 1, 2018. The Institutional Review Board approved all protocols.

Sample

The research team recruited a randomized sample of NHs from each state in the United States. The target goal was 10% of all NHs from each state. A total of 15,653 NHs were identified in the final dataset, resulting in a target sample of 1,565 NHs. The authors oversampled by 15% in each state to account for attrition. Ultimately, 1,977 NH administrators agreed to participate in the survey and 815 returned completed surveys in Year 1 (41.5% response rate). Researchers recruited NH administrators from the same 815 facilities in Year 1 to participate in Year 2, Year 3, and Year 4 of the study using the same recruitment strategy. A total of 188/815 NHs (23% response rate) completed all 4 years of the study. The sample was significantly different (more non-profit, fewer homes in metropolitan areas but slightly more rural facilities) than remaining NHs in the national sample. Differences in facilities based on bed size were nonsignificant (Table 1). The final sample included NHs from every state except Delaware, Hawaii, Idaho, Maryland, and The District of Columbia (DC). A detailed summary of the recruitment strategy has been previously published (Alexander et al., 2017).

Study Sample Versus National Sample According to Organizational Variables

Table 1:

Study Sample Versus National Sample According to Organizational Variables

Survey Measures

The survey measured trends in NH IT sophistication each year. Excellent reliability estimates have been reported previously (Alexander et al., 2017). To answer the research questions, six CAs in the domain of resident care across the three dimensions of IT sophistication were focused on to explore impacts on QMs measured at the same time.

Nursing Home Quality Measures

Six of 16 long-stay QMs reported in the Nursing Home Compare dataset were included. These QMs were selected because they were found to be significantly correlated with total IT sophistication in a previous study (Alexander et al., 2019). The six QMs used in the current study are presented in Figure 1.

Statistical Approach

To account for the dependency of observations (i.e., measures on the same NH over time), a General Estimating Equations (GEE) population-average model with an exchangeable working correlation structure was fit using 752 observations (188 NHs × 4 years). GEE is similar to regression but has multiple advantages, such as using all available pairs of data, providing more reliable parameter estimates, and producing more robust findings (i.e., accurate standard errors) (Hardin & Hilbe, 2012). To address Aim 1, a GEE model predicting each resident care CA score (CA 1–6) by year was fit, controlling for the NH characteristics of bed size (<60, 60 to 120, >120), location (metropolitan, micropolitan, rural, small town), and type of ownership (for profit, non-profit). To address Aim 2, a GEE model predicting each QM score by year and each resident care CA score was fit, controlling for the NH characteristics of bed size, location, and ownership. The significance level was set at α = 0.05. All analyses were performed using SAS version 9.4.

Results

Content Areas and Nursing Home Characteristics

Time in years was a significant predictor of the IT sophistication score for all six CAs while controlling for bed size, location, and ownership (Table A, available in the online version of this article). There was a steady trend (increasing) in IT sophistication scores in all six CAs over the 4-year study period. IT scores in CAs 1, 3, and 5 were not significantly impacted by NH bed size, location, or type of ownership. IT scores in CA 2 were significantly impacted by bed size and type of ownership. NHs with smaller bed size (<60) were estimated to have a 1.05-point lower (95% confidence interval [CI] [3.22, 5.47]) IT sophistication score in CA 2 compared to NHs with >120 beds. This finding indicates NHs with smaller bed sizes have lower IT sophistication in resident care technologies including computerized clinical documents and processes. For-profit NHs were estimated to have a 0.59-point higher score (95% CI [0.01, 1.17]) in CA 2 compared to non-profit NHs. CA 4 and CA 6 were significantly impacted by bed size as well. NHs with <60 beds were estimated to have a 4.12-point lower score (95% CI [−7.07, −1.16]) in CA 4 compared to NHs with >120 beds. Smaller NHs were also estimated to score lower (95% CI [−1.32, −0.03]) in CA 6 compared to NHs with >120 beds.

General Estimating Equations Model Predicting Each Resident Care Content Area (CA) Score (CA 1–6) by Year, Controlling for Nursing Home (NH) Characteristics

Table A:

General Estimating Equations Model Predicting Each Resident Care Content Area (CA) Score (CA 1–6) by Year, Controlling for Nursing Home (NH) Characteristics

Content Areas and Quality Measures

Time in years was a significant predictor of QM score while controlling for bed size, location, and ownership in five of the six QMs examined in the current study (Table B, available in the online version of this article). Year was not a significant predictor of the QM score for 410 (falls with injury). CA 1 was significantly associated with QM 410. For every 1 point increase in CA 1, the QM 410 score is expected to increase by 0.11 points (95% CI [0.02, 0.18]), suggesting that as electronic clinical processes and documents increase (e.g., incident reporting, nursing flowsheets, care planning) in resident care, more falls with injury are detected. QM 401 (activities in daily living increase), QM 402 (pain), and QM 403 (pressure ulcers) were not significantly associated with the six CAs, bed size, location, or type of ownership. QM 408 (depressive symptoms) was associated with NHs with <60 beds and those located in rural areas. NHs with <60 beds were estimated to have a 5.73-point higher score in QM 408 compared to NHs with >120 beds (95% CI [0.04, 2.68]). Rural NHs were estimated to have a 3.42-point lower score in QM 408 compared to NHs located in metropolitan areas (95% CI [−5.70, −1.13]). Lastly, QM 409 (restraints) was found to be significantly associated with bed size and location. NHs with 60 to 120 beds were estimated to have a 0.43-point lower score in QM 409 compared to NHs with >120 beds (95% CI [−0.83, −0.03]). NHs located in rural areas were estimated to have a 0.53-point lower score in QM 409 compared to NHs located in metropolitan areas (95% CI [0.02, 1.03]).

General Estimating Equations Model Predicting Each Quality Measure (QM) Score by Year and Each Resident Care Content Area (CA) Score, Controlling for Nursing Home (NH) Characteristics

Table B:

General Estimating Equations Model Predicting Each Quality Measure (QM) Score by Year and Each Resident Care Content Area (CA) Score, Controlling for Nursing Home (NH) Characteristics

Discussion

Caring for older adults residing in NHs involves a dynamic process of ongoing assessment, transitions, and shifting care plans. Strategic use of IT systems to lessen the burden and improve outcomes is possible; however, a comprehensive and systematic assessment of the capabilities, extent of use, and level of integration of different systems is a prerequisite. The current authors used an instrument designed to systematically measure NH IT adoption and focused specifically on technological components contained in CAs related to resident care. Results from this study offer some insight into how NHs are using IT in resident care and the impact on QMs.

In the current study, the authors examined six CAs representing IT systems used in resident care across the IT domains of capabilities, extent of use, and degree of integration. Time in years was a significant predictor for all six CAs. The biggest change that occurred across the 4 years of the study was in CA 3: the extent of use of technologies in resident care and CA 5: degree to which resident care systems are integrated with other systems in the NH. These findings demonstrate that although IT capabilities are growing in NHs, more substantial growth is occurring in the extent to which these systems are being used and the degree to which they are integrated with other systems. For example, CA 3 includes specific technologies such as telemedicine, sensors, and clinical decision support. This finding is consistent with recent literature reporting more extensive use of technologies such as telemedicine in NHs for specialty consultations (Edirippulige et al., 2013) and use of sensors for early detection of behavioral changes and risk for falls (Cameron et al., 2018; Goerss et al., 2019).

The current study revealed several important differences in CAs that included items specific to physical and occupational therapy (PT/OT). First, NHs with <60 beds scored lower in these CAs compared to NHs with larger bed sizes. Second, the only significant difference in CAs according to profit status was found in CA 2: PT/OT processes that are computerized. For-profit NHs were estimated to have a higher score in CA 2 compared to non-profit NHs. These findings are especially interesting given the recent changes in the Medicare payment model for therapy in skilled nursing facilities. As of October 1, 2019, the Patient Driven Payment Model (PDPM) replaced the Prospective Payment System (PPS) to improve appropriateness of payments by focusing on patient diagnoses and characteristics rather than the volume of services provided (Centers for Medicare & Medicaid Services, 2020). As this policy took effect after the current study took place, the authors were unable to determine what effect if any these changes had on adoption of IT systems specifically intended to support PT/OT. It could be speculated that for-profit NHs took a proactive approach to implementing IT systems in PT/OT in anticipation of these changes. Reassessment of the impact of IT related to PT/OT should be performed as findings could have important implications for policy evaluation.

Examination of the relationship between resident care CAs and QMs yielded several remarkable findings. First, the only CA found to be a significant predictor of any QM was CA 1: clinical processes or documents that are computerized and QM 410 (falls with injury). The authors speculate that more sophisticated computerized processes do not actually result in more resident falls with injury but perhaps aid in accurate reporting of such events, possibly through electronically enhanced incident reporting systems. Second, QM 408 (depressive symptoms) and QM 409 (restraints) were significantly different based on NH location. NHs located in rural areas had a lower percentage of long-stay residents having symptoms of depression and a higher percentage of residents who were physically restrained compared to NHs located in metropolitan areas. This finding is consistent with other urban–rural disparities reported in the literature. For example, NHs located in rural areas have been found to have lower staffing, poorer quality end-of-life care, and are less likely to achieve high star quality ratings, a measure based on performance among QMs (Bowblis et al., 2013; Lutfiyya et al., 2013; Temkin-Greener et al., 2012). Further research is needed to understand how enhanced IT systems can be used to meet the unique needs of rural NH residents.

Limitations

The current longitudinal study relied on survey data collected from NH administrators over 4 years. There may have been bias in the responses to the NH IT sophistication survey. Presumably, some NHs may not participate because they have no technology, which could result in reported overall higher levels of IT capabilities, extent of use, and degree of integration. The analyses were limited to three organizational variables (i.e., NH bed size, location, and ownership). These variables were selected because they demonstrated significance in prior work; however, future studies should include a broader set of organizational variables, such as staffing, bed occupancy, and chain affiliation. Lastly, six of the 16 QMs reported in the Nursing Home Compare dataset were focused on. Although these QMs were selected based on prior work and expertise of the research team, repeating the analysis with all 16 QMs could provide more insight into the relationship between technology and quality.

Implications

The current study findings have important implications for NH leaders. First, these findings highlight the change in NH IT landscape from implementation of discrete systems to extensively using and integrating systems among disciplines. NH leaders' ability to implement IT strategies, to stay ahead of the changing landscape, could provide a competitive edge in the marketplace. Second, this study offers some insight into the relationship between IT and QMs. As policy makers and NH leaders continue to develop objective measures of NH quality, they should consider measures that address IT's impact on NH structure, processes, and outcomes.

Conclusion

The purpose of the current study was to explore the relationship between NH IT systems used in resident care processes and select long-stay QMs. A more granular approach was taken by looking at six specific CAs related to resident care among the IT dimensions of capabilities, extent of use, and degree of integration. Findings revealed IT systems are increasing in sophistication over time, especially in areas directly related to resident care. Important differences were also discovered according to NH bed size, location, and ownership. Continued assessments of NH IT sophistication are important as the impact of technology on quality continues to be evaluated.

References

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Study Sample Versus National Sample According to Organizational Variables

VariableNational SampleStudy Sample (N = 188)p Value
Ownershipa
  For-profit12,002 (76.9)113 (60.1)<0.0001
  Non-profit3,610 (23.1)75 (39.9)<0.0001
Regiona
  Metropolitan (>50,000)10,034 (64.4)105 (55.9)0.0142
  Micropolitan (10,000 to 49,999)2,274 (14.6)24 (12.8)0.4773
  Small town (2,500 to 9,999)1,875 (12)33 (17.5)0.0203
  Rural (<2,500)1,396 (9)26 (13.8)0.0194
Bed sizea
  >1204,474 (28.7)44 (23.4)0.1107
  60 to 1208,162 (52.3)105 (55.9)0.3278
  <602,977 (19)39 (20.7)0.5600

General Estimating Equations Model Predicting Each Resident Care Content Area (CA) Score (CA 1–6) by Year, Controlling for Nursing Home (NH) Characteristics

*CA 1*CA 2*CA 3*CA 4*CA 5*CA 6
Best.p-valueBest.p-valueBest.p-valueBest.p-valueBest.p-valueBest.p-value
Bed size
  <60−0.150.84−1.050.02−3.700.22−4.12<0.01−4.830.10−0.670.04
  60–1200.330.580.320.350.470.85−0.670.590.960.700.220.43
  >120Ref*-----------
Location
  Metro−0.290.660.770.062.130.460.670.630.580.840.450.12
  Micro0.260.740.230.634.600.21−0.790.631.890.590.160.70
  Rural−1.120.17−0.060.91−2.070.58−1.370.46−6.910.05−0.180.65
  SmallRef*-----------
  Town
Ownership
  For-profit−0.750.100.590.04−3.460.11−.0440.66−0.380.11
  Non for-profitRef*-----------
Year
  1−2.22<0.01−1.39<0.01−7.76<0.01−2.99<0.01−10.04<0.01−0.540.01
  2−1.21<0.01−0.74<0.01−3.75<0.01−1.360.05−6.75<0.01−0.170.45
  3−0.85<0.01−0.62<0.01−2.420.020.010.99−3.38<0.010.070.75
  4Ref*-----------

General Estimating Equations Model Predicting Each Quality Measure (QM) Score by Year and Each Resident Care Content Area (CA) Score, Controlling for Nursing Home (NH) Characteristics

QM 401: ADL IncreaseQM 402: PainQM 403: Pressure UlcersQM 408: Depressive SymptomsQM 409: RestraintsQM 410: Falls with Injury
Best.p-valueBest.p-valueBest.p-valueBest.p-valueBest.p-valueBest.p-value
CA 1−0.100.240.250.05−0.040.510.010.89−0.000.950.100.01
CA 20.160.10−0.040.68−0.010.83−0.060.40−0.000.80−0.000.96
CA 3−0.030.09−0.040.09−0.010.23−0.010.45−0.000.40−0.010.17
CA 40.040.300.010.73−0.010.40−0.020.61−0.000.56−0.010.27
CA 50.020.390.010.770.000.79−0.030.130.000.88−0.000.63
CA 6−0.060.510.220.070.030.590.110.38−0.020.35−0.010.88
Bed size
  <60−0.740.522.240.100.310.635.73<0.01−0.390.110.840.11
  60–120−1.310.16−0.090.920.090.831.760.12−0.430.030.020.93
  >120Ref*-----------
Location
  MetroRef*-----------
  Micro0.570.580.330.77−0.090.862.300.16−0.090.44−0.120.76
  Rural1.390.180.600.66−0.290.67−3.42<0.010.530.040.090.84
  Small Town0.620.530.840.360.110.81−0.750.490.400.08−0.040.92
Ownership
  For-profit0.650.37−0.060.940.370.320.080.930.020.87−0.320.30
  Non for-profitRef*-----------
Year
  10.660.152.18<0.01−0.99<0.011.360.040.44<0.01−0.150.53
  21.000.042.12<0.01−0.720.040.860.100.39<0.01−0.040.88
  30.970.03.0890.07−1.07<0.010.200.580.17<0.010.180.48
  4Ref*-----------
Authors

Dr. Powell is Assistant Professor, Dr. Deroche is Assistant Professor, and Dr. Alexander is Associate Dean of Research and Professor, Sinclair School of Nursing, University of Missouri, Columbia, and Mr. Carnahan is Student, Lighthouse Preparatory Academy, Jefferson City, Missouri.

The authors have disclosed no potential conflicts of interest, financial or otherwise. This study was funded by grant R01HS02249 from the Agency for Healthcare Research and Quality (AHRQ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ.

Address correspondence to Kimberly R. Powell, PhD, RN, Assistant Professor, Sinclair School of Nursing, University of Missouri, S 337 School of Nursing, Columbia, MO 65211; e-mail: powellk@missouri.edu.

10.3928/00989134-20200303-02

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