ABSTRACT
Background
Electronic health records (EHRs) use has increasingly become more common in healthcare settings, yet their adoption and use in adult day services centers (ADSCs) remains underexplored. This study investigates EHR trends from 2016 to 2022 and identifies organizational characteristics associated with their use, addressing a critical gap in community‐based long‐term care technology research.
Methods
Data included the 2016, 2018, 2020, and 2022 waves of the National Post‐acute and Long‐term Care Study (NPALS), a nationally representative survey of ADSCs. EHR use was assessed through a single survey question. Independent variables included model type, ownership type, chain status, Medicaid licensure, size, and services provision. Weighted descriptive and survey‐weighted logistic regression analyses were performed using Stata SE v18.
Results
EHR use increased from 23.9% in 2016% to 31.2% in 2022 (p < 0.001). Significantly higher odds of EHR use (p < 0.05) were associated with larger centers (50+ participants, OR = 2.33), medical model centers (OR = 1.81), chains (OR = 1.23), and Medicaid‐licensed centers (OR = 1.46). For‐profit centers had lower odds (OR = 0.58) than nonprofits. Geographical differences were not statistically significant.
Conclusions
While EHR use has grown, a significant digital divide persists. Social model, smaller, smaller, independent, non‐Medicaid‐licensed centers, and for‐profit centers are significantly less likely to adopt EHRs. Further research could examine the role of policies, funding, and decision‐making processes that may present barriers or incentives to meaningful EHR implementation and whether they provide benefits for care coordination in ADSCs.
Keywords: electronic health records, health technology, long‐term care, National Post‐acute Long‐term Care Study, older adult, trend analysis
Summary
Electronic health record (EHR) use in adult day services centers (ADSCs) increased from 23.9% in 2016% to 31.2% in 2022, with statistically significant associations with center characteristics.
ADSCs using EHRs tended to be medical model centers, chains, nonprofit, larger with 26 or more participants, Medicaid‐licensed, and provide pharmacy services.
Geographical differences in EHR use were not observed.
1. Introduction
Electronic health records (EHRs) have become important tools in mainstream healthcare, intending to improve clinical efficiency and enhance patient care [1]. The enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009 was a pivotal catalyst, authorizing “meaningful use” incentives that drove widespread EHR adoption across many healthcare sectors [2, 3]. However, the financial incentives were primarily targeted at hospitals and physician clinics, leaving many long‐term care providers ineligible. Consequently, home‐ and community‐based services (HSBC) such as adult day services centers (ADSCs), have lagged significantly behind in the digital transition [1, 4].
ADSCs provide critical post‐acute and chronic illness care to approximately 197,700 participants in the United States in 2022, 68% of whom were aged 65 and above with complex care needs [5]. Despite their critical role, EHR adoption in ADSCs remains one of the least studied areas of health information technology. Extensive research on increases in EHRs has largely focused on hospitals and physician clinics [1, 2, 3], nursing homes [6], and residential care or assisted living settings [7, 8, 9, 10, 11]. Therefore, little is known of EHR use in ADSCs. Previous qualitative work has highlighted the fragmented communication between ADSCs and primary care [12], and earlier cross‐sectional data from 2018 suggested significant disparities in adoption based on center characteristics [4].
There is a gap in research examining how EHR adoption has evolved within the ADSC sector over time, particularly during the COVID‐19 pandemic. The pandemic transformed healthcare delivery and technology use, accelerating the expansion of telehealth as centers adapted to closures, staffing disruptions, and participant illness. Descriptive analyses in 2016 show 2016 the overall prevalence of EHR use was 23.9%, which ranged from 32.7% among medical model centers to just 9.8% among social model centers [11, 13]. In 2020, 28.6% of centers used EHRs, one‐third of which also used telemedicine due to COVID‐19 [14]. However, Little is known about whether these patterns of EHR use have changed in the years since and highlights the importance of examining EHR use across multiple time points.
Our study seeks to address this literature gap by examining nationwide trends and evaluating the organizational characteristics that may influence EHR use within ADSCs. This study uses the comprehensive, pooled cross‐sectional data from the National Center for Health Statistics' (NCHS) National Post‐acute and Long‐term Care Study (NPALS) for 2016, 2018, 2020, and 2022. This is the only nationally representative survey of post‐acute and long‐term care that allows us to examine trends before and after the COVID‐19 pandemic, and that also includes a variety of key ADSC characteristics. The data were analyzed to: (1) provide nationally representative percentages of EHR use among ADSC providers during multiple years; (2) evaluate nationwide trends in EHR use, and (3) identify organizational characteristics associated with EHR use. Through these objectives, we seek to inform policies and practices that can address the public health need for digital modernization, like EHR integration, in long‐term care.
2. Methods
2.1. Data
This study uses data from NCHS's NPALS (previously called the National Study of Long‐term Care Providers (NSLTCP) from the years 2016, 2018, 2020, and 2022.
NCHS fielded a census of ADSCs in the United States in 2016 and 2020 and surveyed a random sample of ADSCs in 2018 and 2022. To be eligible, ADSCs had to meet certain criteria, including licensure or certification, an average daily attendance of one or more participants, and enrollment of one or more participants at the designated location at the time of the survey. Data were collected through mail, web, and computer‐assisted telephone interviews. ADSC directors or staff knowledgeable of the centers' operations answered provider questionnaires, which consisted of four sections, including organizational background information, participant demographics and health characteristics, staffing profiles, and services offered. More detailed information about NPALS methodology and questionnaires is available online at www.cdc.gov/nchs/npals/questionnaires/index.html.
3. Measurement
EHR use was measured by the question: “An Electronic Health Record (EHR) is a computerized version of the participant's health and personal information used in the management of the participant's healthcare. Other than for accounting or billing purposes, does this adult day services center use Electronic Health Records? Yes or No.” The independent variables include U.S. Census region, metropolitan statistical area (MSA) status, chain status, ownership type (nonprofit or for‐profit), number of people currently enrolled, Medicaid licensure, model type (medical model designed to meet the medical and health needs of participants or social model designed to meet social needs only), services provided by employee, arrangement, or referral (nursing, pharmacy, social work, therapeutic, and dietary), and center specialization in a particular condition. All independent variables are available across all survey years.
Decisions regarding variable selection and recoding (e.g., collapsing categories) were guided by conceptual reasoning and considerations related to cell size. For example, the ownership status variable included four response options, which were collapsed into two categories—for‐profit versus nonprofit and government‐owned ADSCs. This approach simplifies the interpretation of results and prevents small cell sizes, thereby mitigating disclosure risk and enhancing the reliability of estimates. For the years 2016‐2020, the percentage of missing data is relatively low, with all variables having less than 10% missing (most with less than 3%). In 2022, service variables had over 10% missing data. For these analyses, missing data are excluded on a case‐by‐case basis.
4. Analyses
The analyses include descriptive statistics for EHR use and the geographical and organizational characteristics for each survey year, with pairwise chi‐square tests for differences between 2016 and subsequent years. Data from all survey years were combined to perform pooled cross‐sectional logistic regression analyses on EHR use with survey year as an independent variable in the model, to test for trends and associated characteristics [15, 16]. We ran survey‐weighted logistic regression first with year only to test for an overall trend, and then with covariates to assess associations with the selected characteristics. We calculated the marginal percentages of EHR use over time based on the full logistic regression model and present the results in Figure 1. Sensitivity analyses were conducted to examine any interactions between survey year and independent variables, where appropriate. Complex survey weights were used in all analyses to provide nationally representative estimates that account for unknown eligibility of ADSCs and non‐response. All analyses adhere to NCHS guidelines for confidentiality and reliability of estimates [17]. Statistical significance of chi‐square and logistic regression odds ratios was determined using a two‐tailed a priori statistical level of p < 0.05. Analyses were conducted using StataSE version 18.
Figure 1.
Adjusted estimated percentages of electronic health record use among adult day services centers, 2016–2022.
5. Results
Table 1 shows the percentages for EHR use and other characteristics for each survey year. In 2016, 23.9% of centers used EHRs, which increased to 30.2% in 2018, and remained similar at 29.1% in 2020% and 31.2% in 2022 (p < 0.05) (Table 1). A logistic regression confirmed an overall increase in EHR use (F = 7.97, p < 0.000) and the logistic regression controlling for key organizational characteristics further identified the increased odds of using an EHR (Table 2). Compared to 2016, there was a statistically significant increase in EHR use in 2018 (OR = 1.48, p < 0.001), 2020 (OR = 1.70, p < 0.001), and 2022 (OR = 1.63, p < 0.001). Figure 1 shows the adjusted predicted percentages of EHR use from 22.7% in 2016% to 31.3% in 2022. Organizational characteristics associated with EHR adoption included: medical model centers compared to social model centers (OR = 1.81, p < 0.001); chain‐affiliated centers compared to non‐affiliated centers (OR = 1.23, p < 0.05); for profit compared to nonprofit (OR = 0.58, p < 0.001); centers serving 26 or more participants compared to those serving fewer than 25 participants (ORs=1.28 and 2.33, p < 0.05); Medicaid licensed centers compared to non‐Medicaid licensed centers (OR = 1.46, p < 0.01); and centers providing pharmacy services compared to those not providing pharmacy services (OR = 1.35, p < 0.01). There were no other significant associations between EHRs and the other types of services provided at the center. Ad hoc tests of interactions between covariates (e.g., model type) and year did not show differing trends in EHR use.
Table 1.
Electronic health records use and organizational characteristics of adult day services centers over time, 2016–2022.
| Characteristic | 2016 (Ref) | 2018 | 2020 | 2022 |
|---|---|---|---|---|
| % (95% CI) | % (95% CI) | % (95% CI) | % (95% CI) | |
| Electronic health records use | ||||
| No | 76.1 (75.1–77.1) | 69.8* (66.3–73.1) | 70.9* (69.3–72.5) | 68.8* (64.1–73.3) |
| Yes | 23.9 (22.9–24.9) | 30.2* (26.9‐33.7) | 29.1* (27.5–30.7) | 31.2* (26.7–35.9) |
| Model type | ||||
| Social model | 46.8 (45.7–47.8) | 48.0 (44.6–51.4) | 54.9* (53.3–56.6) | 51.7* (47.0–56.3) |
| Medical model | 53.2 (52.2–54.3) | 52.0 (48.6–55.4) | 45.1* (43.4–46.7) | 48.3* (43.7–53.0) |
| US census region | ||||
| Northeast | 20.1 (20.1–20.2) | 19.3* (19.1–19.5) | 18.4* (18.4–18.4) | 18.3* (18.1–18.5) |
| South | 16.9 (16.8–16.9) | 15.5* (15.3–15.7) | 14.2* (14.1–14.2) | 13.3* (13.1–13.4) |
| Midwest | 32.2 (32.1–32.3) | 33.3* (33.0–33.6) | 34.2* (34.1–34.3) | 36.6* (36.1–37.1) |
| West | 30.8 (30.7–30.8) | 32.0* (31.7–32.2) | 33.2* (33.2–33.3) | 31.8* (31.4–32.2) |
| Metropolitan statistical area | ||||
| No | 15.2 (14.5–15.9) | 15.1 (12.6–17.9) | 13.8* (13.5–14.0) | 10.9* (8.5–13.7) |
| Yes | 84.8 (84.1–85.5) | 84.9 (82.1–87.4) | 86.2* (86.0–86.5) | 89.1* (86.3–91.5) |
| Chain status | ||||
| No | 57.4 (56.2–58.5) | 57.3 (53.7–60.9) | 59.9* (58.1–61.6) | 57.4 (52.4–62.2) |
| Yes | 42.6 (41.5–43.8) | 42.7 (39.1–46.3) | 40.1* (38.4–41.9) | 42.6 (37.8–47.6) |
| Ownership status | ||||
| Nonprofit | 55.3 (54.2–56.4) | 57.7 (54.1–61.2) | 54.5 (52.8–56.2) | 57.4 (52.7–62.0) |
| For profit | 44.7 (43.6–45.8) | 42.3 (38.8–45.9) | 45.5 (43.8–47.2) | 42.6 (38.0–47.3) |
| Number of enrolled participants | ||||
| 1–25 | 27.7 (26.7–28.6) | 22.9* (20.0–26.0) | 33.7* (32.2–35.2) | 27.3 (23.4–31.7) |
| 26–50 | 29.5 (28.4–30.5) | 30.9 (27.6–34.4) | 27.6* (26.0–29.3) | 29.8 (25.7–34.2) |
| > 51 | 42.9 (41.8–43.9) | 46.3 (42.7–49.8) | 38.7* (37.1–40.3) | 42.9 (38.3–47.6) |
| Medicaid licensure | ||||
| No | 23.1 (22.2–24.0) | 25.1 (22.3‐28.1) | 27.9* (26.5–29.4) | 25.6 (21.8–29.8) |
| Yes | 76.9 (76.0–77.8) | 74.9 (71.9–77.7) | 72.1* (70.6–73.5) | 74.4 (70.2–78.2) |
| Specialized in a specific condition | ||||
| No | 77.5 (76.5–78.5) | 72.1* (68.8–75.2) | 69.7* (68.1–71.3) | 72.1* (67.7–76.2) |
| Yes | 22.5 (21.5–23.5) | 27.9* (24.8–31.2) | 30.3* (28.7–31.9) | 27.9* (23.8–32.3) |
| Dietary and nutrition services provided | ||||
| No | 22.9 (21.9–24.0) | 28.5* (25.4–31.8) | 31.6* (30.0–33.3) | 28.7* (24.4–33.4) |
| Yes | 77.1 (76.0–78.1) | 71.5* (68.2–74.6) | 68.4* (66.7–70.0) | 71.3* (66.6–75.6) |
| Nursing services provided | ||||
| No | 25.7 (24.6–26.7) | 28.0 (25.1–31.2) | 33.6* (32.0–35.3) | 27.3 (23.2–31.8) |
| Yes | 74.3 (73.3–75.4) | 72.0 (68.8–74.9) | 66.4* (64.7–68.0) | 72.7 (68.2–76.8) |
| Pharmacy services provided | ||||
| No | 52.1 (50.9–53.3) | 54.7 (51.1–58.2) | 55.2* (53.4–57.0) | 54.8 (49.9–59.7) |
| Yes | 47.9 (46.7–49.1) | 45.3 (41.8–48.9) | 44.8* (43.0–46.6) | 45.2 (40.3–50.1) |
| Social worker services provided | ||||
| No | 26.5 (25.4–27.6) | 27.6 (24.4–31.0) | 35.3* (33.6–37.0) | 35.2* (30.5–40.1) |
| Yes | 73.5 (72.4–74.6) | 72.4 (69.0–75.6) | 64.7* (63.0–66.4) | 64.8* (59.9–69.5) |
| Therapeutic services provided | ||||
| No | 30.4 (29.2–31.5) | 32.1 (28.7–35.6) | 39.7* (37.9–41.5) | 32.2 (27.8–37.1) |
| Yes | 69.6 (68.5–70.8) | 67.9 (64.4–71.3) | 60.3* (58.5–62.1) | 67.8 (62.9–72.2) |
| Unweighted N | 2766 | 652 | 1762 | 356 |
| Response rate | 61.8% | 50.0% | 43.0% | 40.0% |
Note: CI is confidence interval. An electronic health record (EHR) is a computerized version of the participant's health and personal information used in the management of the participant's health care, used for other than accounting or billing purposes. Medical model centers only, primarily, or equally meet the health/medical needs of participants; social model centers only or primarily meet social/recreational needs. Specialized centers serve only participants with particular diagnoses, conditions, or disabilities. Services are provided by employees, arrangement with outside providers, or by referral. Response rates are calculated using AAPOR's Response Rate 4.
Statistically significantly different from 2016, p < 0.05.
Source: National Center for Health Statistics, National Study of Long‐term Care Providers 2016, 2018, and National Post‐acute and Long‐term Care Study, 2020, 2022.
Table 2.
Logistic regression model of electronic health records use among adult day services centers on survey year and organizational characteristics.
| Odds ratio | SE | p value | 95% CI | ||
|---|---|---|---|---|---|
| Survey year | |||||
| 2016 | reference | ||||
| 2018 | 1.48 | 0.16 | 0.000 | 1.2 | 1.83 |
| 2020 | 1.71 | 0.13 | 0.000 | 1.47 | 1.99 |
| 2022 | 1.63 | 0.22 | 0.000 | 1.25 | 2.12 |
| Model type | |||||
| Social model | reference | ||||
| Medical model | 1.81 | 0.19 | 0.000 | 1.48 | 2.21 |
| US Census region | |||||
| Northeast | reference | ||||
| South | 1.28 | 0.18 | 0.079 | 0.97 | 1.67 |
| Midwest | 0.88 | 0.1 | 0.262 | 0.7 | 1.1 |
| West | 1.2 | 0.15 | 0.160 | 0.93 | 1.54 |
| Metropolitan statistical area | |||||
| No | reference | ||||
| Yes | 1.00 | 0.13 | 0.994 | 0.78 | 1.28 |
| Chain status | |||||
| No | reference | ||||
| Yes | 1.23 | 0.1 | 0.017 | 1.04 | 1.45 |
| Ownership status | |||||
| Nonprofit | reference | ||||
| For profit | 0.58 | 0.05 | 0.000 | 0.49 | 0.69 |
| Number of enrolled participants | |||||
| 1–25 | reference | ||||
| 26–50 | 1.28 | 0.15 | 0.043 | 1.01 | 1.62 |
| > 51 | 2.33 | 0.27 | 0.000 | 1.85 | 2.93 |
| Medicaid licensure | |||||
| No | reference | ||||
| Yes | 1.46 | 0.18 | 0.002 | 1.15 | 1.85 |
| Specialized in a specific condition | |||||
| No | reference | ||||
| Yes | 1.02 | 0.11 | 0.875 | 0.83 | 1.25 |
| Dietary and nutrition services provided | |||||
| No | reference | ||||
| Yes | 1.26 | 0.19 | 0.115 | 0.94 | 1.68 |
| Nursing services provided | |||||
| No | reference | ||||
| Yes | 1.07 | 0.16 | 0.640 | 0.8 | 1.42 |
| Pharmacy services provided | |||||
| No | reference | ||||
| Yes | 1.35 | 0.14 | 0.005 | 1.09 | 1.66 |
| Social work services provided | |||||
| No | reference | ||||
| Yes | 1.28 | 0.18 | 0.076 | 0.97 | 1.67 |
| Therapeutic services provided | |||||
| No | reference | ||||
| Yes | 1.25 | 0.17 | 0.107 | 0.95 | 1.63 |
| Constant | 0.05 | 0.01 | 0.000 | 0.03 | 0.08 |
Note: SE is standard error. CI is a confidence interval. An electronic health record (EHR) is a computerized version of the participant's health and personal information used in the management of the participant's health care, used for other than accounting or billing purposes. Medical model centers only, primarily, or equally meet the health/medical needs of participants; social model centers only or primarily meet social/recreational needs. Specialized centers serve only participants with particular diagnoses, conditions, or disabilities. Services are provided by employees, arrangement with outside providers, or by referral.
Wald‐adjusted F (19, 4943) = 17.9, p < 0.000.
Source: National Center for Health Statistics, National Study of Long‐term Care Providers 2016, 2018, and National Post‐acute and Long‐term Care Study, 2020, 2022.
6. Discussion
This study is the first to report nationally representative estimates of EHR use among ADSCs over nearly one decade, incorporating the most recent and only national data source on ADSCs. These findings show an increase in EHR use by ADSCs between 2016 and 2018, and this increase was stable through 2022, indicating about one‐third of ADSCs use EHRs. EHR use was associated with a combination of factors, including time, organizational characteristics, including medical model type, larger center size, Medicaid licensure, belonging to a chain, nonprofit status, and the provision of pharmacy services. These organizational characteristics associated with EHR use did not seem to drive the increase in EHR use from 2016 to 2022. There were no differences in the odds of EHR use by region, MSA, or many of the types of services provided at the center. Increases in EHR use may have slowed from 2018 to 2022, but did not decline. While this study does not explain this stall, potential reasons include: (1) impacts of COVID‐19 that led to center closures, reduced enrollment, and financial strain [18]; (2) the lack of financial incentives for EHR adoption among ADSCs, unlike other healthcare settings [1]; and (3) a saturation point where centers most likely to be incentivized or benefit from EHRs (such as medical model, Medicaid licensed, for profit, and larger centers) adopted them earlier on. The remaining two‐thirds of centers with characteristics negatively associated with EHR use (e.g., social model, nonprofit, non‐Medicaid licensed, smaller centers) may perceive limited value or capacity to use EHR. The COVID‐19 pandemic represents a unique period in history, highlighting the need for further research to understand how external factors may have influenced EHR use in long‐term care settings during this period.
This study drew on the strengths of NPALS data, a comprehensive and nationally representative survey of ADSCs; however, we acknowledge some limitations to this study. First, variation exists in question wording and response options across survey years, potentially affecting the comparability of the different time periods. Of note, the question assessing EHR use remained consistent across survey years and instructed respondents to exclude electronic systems used solely for accounting or billing purposes, thereby supporting comparability of the measure over time. Second, in 2018 and 2022, data were obtained from a sample utilizing provider and user questionnaire modules, but in 2016 and 2020, data were collected from a census of ADSCs with only a provider questionnaire. In 2018 and 2022, the sample sizes were smaller, and estimates had larger confidence intervals. Third, response rates declined across the study period, and in 2022, there was a greater overall percentage of missing data for services and staffing variables. This increase in missing data may be attributed to center closures and challenges with restaffing and service provision after 2020. All estimates are calculated using complex survey weights to account for potential non‐response bias that may be introduced by these limitations. Fourth, the study is based on cross‐sectional panel data, the trends observed are not center‐level longitudinal changes. Although it is possible for the same ADSCs to respond to the survey in multiple years, the cross‐sectional nature of the data does not allow for the determination of causality in EHR adoption or trends.
Continued data collection on EHR adoption rates, use, and measures of perceived value and cost‐effectiveness of EHRs will help further investigate the trends in this important healthcare technology.
Author Contributions
Yawen Li: writing – original draft. Jessica P. Lendon: writing – original draft. Shannon Kindilien: writing – original draft. Christine Caffrey: writing – original draft.
Funding
The authors have nothing to report.
Disclosure
The findings and conclusions in this paper are those of the authors and do not necessarily represent the official position of the National Center for Health Statistics or the Centers for Disease Control and Prevention.
Conflicts of Interest
The authors declare no conflicts of interest.
Transparency Statement
The lead authors, Yawen Li and Jessica P. Lendon, affirm that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
Acknowledgments
We thank the National Center for Health Statistics for the data and for providing valuable insights and feedback during the proposal and manuscript approval stages that greatly contributed to the improvement of this study.
Contributor Information
Yawen Li, Email: yawen.li@csusb.edu.
Jessica P. Lendon, Email: jlendon@cdc.gov.
Data Availability Statement
The data used in this study were drawn from restricted‐use files accessed through the National Center for Health Statistics (NCHS) Research Data Center (https://www.cdc.gov/rdc/index.html). Selected NPALS data for 2018 and 2022 are also available as Public Use Files at https://www.cdc.gov/nchs/npals/questionnaires/index.html.
References
- 1. Gold M. and McLaughlin C., “Assessing HITECH Implementation and Lessons: 5 Years Later,” Milbank Quarterly 94, no. 3 (2016): 654–687, 10.1111/1468-0009.12214. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Adler‐Milstein J., DesRoches C. M., Kralovec P., et al., “Electronic Health Record Adoption in US Hospitals: Progress Continues, But Challenges Persist,” Health Affairs 34, no. 12 (2015): 2174–2180, 10.1377/hlthaff.2015.0992. [DOI] [PubMed] [Google Scholar]
- 3. Adler‐Milstein J., Raphael K., Bonner A., Pelton L., and Fulmer T., “Hospital Adoption of Electronic Health Record Functions to Support Age‐Friendly Care: Results From a National Survey,” Journal of the American Medical Informatics Association 27, no. 8 (2020): 1206–1213, 10.1093/jamia/ocaa129. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Li Y., Chok J., Cui G., Roosan D., and Shultz K., “Electronic Health Record Adoption Among Adult Day Services: Findings From the National Study of Long‐Term Care Providers,” Journal of the American Geriatrics Society 71, no. 12 (2023): 3941–3943, 10.1111/jgs.18549. [DOI] [PubMed] [Google Scholar]
- 5. National Center for Health Statistics . Biennial Overview of Post‐acute and Long‐term Care in the United States: Data from the National Post‐acute and Long‐term Care Study. (2022). https://www.cdc.gov/nchs/npals/webtables/overview.htm.
- 6. Alvarado C. S., Zook K., and Henry J., “Electronic Health Record Adoption and Interoperability among U.S. Skilled Nursing Facilities in 2016.” ASTP Health IT Data Brief (Office of the Assistant Secretary for Technology Policy, 2017). http://www.ncbi.nlm.nih.gov/books/NBK610657/. [PubMed] [Google Scholar]
- 7. Caffrey C., Cairns C., and Rome V., “Trends in Electronic Health Record Use Among Residential Care Communities: United States, 2012, 2014, and 2016,” National Health Statistics Reports, no. 140 (2020): 1–10. [PubMed] [Google Scholar]
- 8. Caffrey C. and Sengupta M., “Quickstats:Percentage of Residential Care Communities* That Use Electronic Health Records, by Community Bed Size — United States, 2018, 2020, and 2022†,” MMWR. Morbidity and Mortality Weekly Report 73, no. 17 (2024): 409, 10.15585/mmwr.mm7317a6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Burke R. E., Jones C. D., Levy C., and Ginde A. A., “Electronic Communication Capabilities of Residential Care Facilities at Times of Transition,” Journal of the American Geriatrics Society 62, no. 7 (2014): 1381–1383, 10.1111/jgs.12905. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Gaskin S., Georgiou A., Barton D., and Westbrook J., “Examining the Role of Information Exchange in Residential Aged Care Work Practices‐A Survey of Residential Aged Care Facilities,” BMC Geriatrics 12, no. 1 (2012): 40, 10.1186/1471-2318-12-40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. National Center for Health Statistics . Tables on Use of Electronic Health Records and Health Information Exchange among Adult Day Services Centers and Residential Care Communities from the 2016 National Study of Long‐Term Care Providers, www.cdc.gov/nchs/data/nsltcp/EHR-HIE-use-among-adsc-rcc-2016.pdf.
- 12. Zhong J., Boafo J., Brody A. A., Wu B., and Sadarangani T., “A Qualitative Analysis of Communication Workflows Between Adult Day Service Centers and Primary Care Providers,” Journal of the American Medical Informatics Association 29, no. 5 (2022): 882–890, 10.1093/jamia/ocab284. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Rome V., Lendon J. P., and Harris‐Kojetin L., National Center for Health Statistics . Differences in Characteristics of Adult Day Services Centers, by Level of Medical Service Provision,” Vital and Health Statistics 3, no. 45 (2020): 1–37. [Google Scholar]
- 14. Peters Z. J., Lendon J., Caffrey C., Myrick K. L., Mahar M., and DeFrances C. J., “Telemedicine Use During the COVID‐19 Pandemic by Office‐Based Physicians and Long‐Term Care Providers” (National Health Statistics Reports, 2024). 10.15620/cdc/159282. [DOI] [PMC free article] [PubMed]
- 15. Ellis Hilts K., Gibson P. J., Blackburn J., Yeager V. A., Halverson P. K., and Menachemi N., “Institutional Factors Associated With Hospital Partnerships for Population Health: A Pooled Cross‐Sectional Analysis,” Health Care Management Review 47, no. 3 (2022): 254–262, 10.1097/HMR.0000000000000325. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Ingram D. D., Malec D. J., Makuc D. M., et al., “National Center for Health Statistics Guidelines for Analysis of Trends. National Center for Health Statistics,” Vital Health Statistics 2, no. 179 (2018): 1–71, https://www.cdc.gov/nchs/data/series/sr_02/sr02_179.pdf. [PubMed] [Google Scholar]
- 17. Parker J. D., Talih M., Malec D. J., et al., “National Center for Health Statistics Data Presentation Standards for Proportions. National Center for Health Statistics,” Vital Health Statistics 2, no. 175 (2017): 1–22, https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf. [PubMed] [Google Scholar]
- 18. Tsai T., Boazak M., and Hinz E. R. M., “Increased Clinician Time Using Electronic Health Records During COVID‐19 Pandemic,” AMIA. Annual Symposium proceedings. AMIA Symposium 2021 (2021): 1159–1168. [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data used in this study were drawn from restricted‐use files accessed through the National Center for Health Statistics (NCHS) Research Data Center (https://www.cdc.gov/rdc/index.html). Selected NPALS data for 2018 and 2022 are also available as Public Use Files at https://www.cdc.gov/nchs/npals/questionnaires/index.html.