Building consensus toward a national nursing home information technology maturity model

Optimizing Response Rates to Examine Health IT Maturity and Nurse Practitioner Care Environments in US Nursing Homes: Mixed Mode Survey Recruitment Protocol

BACKGROUND

Survey-driven research is a reliable method for large-scale data collection. Investigators incorporating mixed-mode survey designs report benefits for survey research including greater engagement, improved survey access, and higher response rate. Mix-mode survey designs combine 2 or more modes for data collection including web, phone, face-to-face, and mail. Types of mixed-mode survey designs include simultaneous (ie, concurrent), sequential, delayed concurrent, and adaptive. This paper describes a research protocol using mixed-mode survey designs to explore health IT (HIT) maturity and care environments reported by administrators and nurse practitioners (NPs), respectively, in US nursing homes (NHs).

OBJECTIVE

The aim of this study is to describe a research protocol using mixed-mode survey designs in research using 2 survey tools to explore HIT maturity and NP care environments in US NHs.

METHODS

We are conducting a national survey of 1400 NH administrators and NPs. Two data sets (ie, Care Compare and IQVIA) were used to identify eligible facilities at random. The protocol incorporates 2 surveys to explore how HIT maturity (survey 1 collected by administrators) impacts care environments where NPs work (survey 2 collected by NPs). Higher HIT maturity collected by administrators indicates greater IT capabilities, use, and integration in resident care, clinical support, and administrative activities. The NP care environment survey measures relationships, independent practice, resource availability, and visibility. The research team conducted 3 iterative focus groups, including 14 clinicians (NP and NH experts) and recruiters from 2 national survey teams experienced with these populations to achieve consensus on which mixed-mode designs to use. During focus groups we identified the pros and cons of using mixed-mode designs in these settings. We determined that 2 mixed-mode designs with regular follow-up calls (Delayed Concurrent Mode and Sequential Mode) is effective for recruiting NH administrators while a concurrent mixed-mode design is best to recruit NPs.

RESULTS

Participant recruitment for the project began in June 2023. As of April 22, 2024, a total of 98 HIT maturity surveys and 81 NP surveys have been returned. Recruitment of NH administrators and NPs is anticipated through July 2025. About 71% of the HIT maturity surveys have been submitted using the electronic link and 23% were submitted after a QR code was sent to the administrator. Approximately 95% of the NP surveys were returned with electronic survey links.

CONCLUSIONS

Pros of mixed-mode designs for NH research identified by the team were that delayed concurrent, concurrent, and sequential mixed-mode methods of delivering surveys to potential participants save on recruitment time compared to single mode delivery methods. One disadvantage of single-mode strategies is decreased versatility and adaptability to different organizational capabilities (eg, access to email and firewalls), which could reduce response rates.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/56170.

Assessing Associations Between Health Information Technology Maturity and Nursing Home Survey Deficiencies

One in three nursing home (NH) residents experience adverse events. One strategy for safer NH care is health information technology (HIT). Two national NH surveys measuring HIT maturity were administered in 2020 (N = 719) and 2021 (N = 312). Quarterly NH survey deficiencies from the same years were linked to HIT maturity surveys. Descriptive statistics and logistic regression were used in analysis. NHs were of similar size and location, with more for-profit facilities. Most (67.5% and 61.9%, respectively) NH administrators reported having capabilities to share data internally within their facility, and not externally. Mean HIT maturity scores increased from Year 1 to Year 2. Over 2 years, 5,406 deficiencies were reported, mostly (31.3%) for nutrition and dietary deficiencies. There were negative associations between HIT maturity and deficiency scope. With a 1-unit increase in HIT maturity, relative risk of widespread scope decreased by 14%. Among covariates, bed size, staffing, and year were significant factors associated with deficiency scope. [Journal of Gerontological Nursing, 50(1), 8-14.].

Natural Language Processing Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review

OBJECTIVES

To determine the scope of the application of natural language processing to free-text clinical notes in post-acute care and provide a foundation for future natural language processing-based research in these settings.

DESIGN

Scoping review; reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.

SETTING AND PARTICIPANTS

Post-acute care (ie, home health care, long-term care, skilled nursing facilities, and inpatient rehabilitation facilities).

METHODS

PubMed, Cumulative Index of Nursing and Allied Health Literature, and Embase were searched in February 2023. Eligible studies had quantitative designs that used natural language processing applied to clinical documentation in post-acute care settings. The quality of each study was appraised.

RESULTS

Twenty-one studies were included. Almost all studies were conducted in home health care settings. Most studies extracted data from electronic health records to examine the risk for negative outcomes, including acute care utilization, medication errors, and suicide mortality. About half of the studies did not report age, sex, race, or ethnicity data or use standardized terminologies. Only 8 studies included variables from socio-behavioral domains. Most studies fulfilled all quality appraisal indicators.

CONCLUSIONS AND IMPLICATIONS

The application of natural language processing is nascent in post-acute care settings. Future research should apply natural language processing using standardized terminologies to leverage free-text clinical notes in post-acute care to promote timely, comprehensive, and equitable care. Natural language processing could be integrated with predictive models to help identify patients who are at risk of negative outcomes. Future research should incorporate socio-behavioral determinants and diverse samples to improve health equity in informatics tools.

What is the level of information technology maturity in Ontario's long-term care homes? A cross-sectional survey study protocol

INTRODUCTION

The number of Canadians 75 years and older is expected to double over the next 20 years, putting continuing care systems such as long-term care (LTC) homes under increasing pressure. Health information technology (IT) has been found to improve the quality, safety and efficiency of care in numerous clinical settings and could help optimise LTC for residents. However, the level of health IT adoption in Ontario's LTC homes is unknown and, as a result, requires an accurate assessment to provide a baseline understanding for future planning.

METHODS AND ANALYSIS

We will use a cross-sectional design to investigate the level of IT maturity in Ontario's LTC homes. IT maturity will be assessed with the LTC IT Maturity Instrument, a validated survey examining IT capabilities, the extent of IT use and degree of internal/external IT integration across the domains of resident care, clinical support and administrative activities. All LTC homes in Ontario will be invited to participate. The Director of Care for each home will be directly contacted for recruitment. The survey will be distributed online (or by paper, if preferred) to LTC homes and completed by a staff member designated by the LTC to be knowledgeable about its IT systems. Analyses will consist of descriptive statistics characterising IT maturity across LTC homes and inferential statistics to examine the association between key facility-level characteristics (size, ownership, rurality) and IT maturity.

ETHICS AND DISSEMINATION

This study was reviewed by the Ottawa Health Science Network Research Ethics Board and was exempt from full ethics review. Findings will be disseminated through peer-reviewed publication and presentations to the scientific community and stakeholders. Dissemination of our findings will not only inform provincial planning for harnessing the potential of technology in LTC but may also enable quality improvement initiatives in individual LTC homes.

Digital health and capability maturity models-a critical thematic review and conceptual synthesis of the literature

OBJECTIVE

A literature review of capability maturity models (MMs) to inform the conceptualization, development, implementation, evaluation, and mainstreaming of MMs in digital health (DH).

METHODS

Electronic databases were searched using "digital health," "maturity models," and related terms based on the Digital Health Profile and Maturity Assessment Toolkit Maturity Model (DHPMAT-MM). Covidence was used to screen, identify, capture, and achieve consensus on data extracted by the authors. Descriptive statistics were generated. A thematic analysis and conceptual synthesis were conducted.

FINDINGS

Diverse domain-specific MMs and model development, implementation, and evaluation methods were found. The spread and pattern of different MMs verified the essential DH foundations and five maturity stages of the DHPMAT-MM. An unanticipated finding was the existence of a new category of community-facing MMs. Common characteristics included:1. A dynamic lifecycle approach to digital capability maturity, which is:a. responsive to environmental changes and may improve or worsen over time;b. accumulative, incorporating the attributes of the preceding stage; andc. sequential, where no maturity stage must be skipped.2. Sociotechnical quality improvement of the DH ecosystem and MM, which includes:a. investing in the organization's human, hardware, and software resources andb. a need to engage and improve the DH competencies of citizens.

CONCLUSIONS

The diversity in MMs and variability in methods and content can create cognitive dissonance. A metamodel like the DHPMAT-MM can logically unify the many domain-specific MMs and guide the overall implementation and evaluation of DH ecosystems and MMs over the maturity lifecycle.

Examining Structural Disparities in U.S. Nursing Homes: A National Survey of Health Information Technology Maturity (Preprint)

Background

There are 15,632 nursing homes (NHs) in the United States. NHs continue to receive significant policy attention due to high costs and poor outcomes of care. One strategy for improving NH care is use of health information technology (HIT). A central concept of this study is HIT maturity, which is used to identify adoption trends in HIT capabilities, use and integration within resident care, clinical support, and administrative activities. This concept is guided by the Nolan stage theory, which postulates that a system such as HIT moves through a series of measurable stages. HIT maturity is an important component of the rapidly changing NH landscape, which is being affected by policies generated to protect residents, in part because of the pandemic.

Objective

The aim of this study is to identify structural disparities in NH HIT maturity and see if it is moderated by commonly used organizational characteristics.

Methods

NHs (n=6123, >20%) were randomly recruited from each state using Nursing Home Compare data. Investigators used a validated HIT maturity survey with 9 subscales including HIT capabilities, extent of HIT use, and degree of HIT integration in resident care, clinical support, and administrative activities. Each subscale had a possible HIT maturity score of 0-100. Total HIT maturity, with a possible score of 0-900, was calculated using the 9 subscales (3 x 3 matrix). Total HIT maturity scores equate 1 of 7 HIT maturity stages (stages 0-6) for each facility. Dependent variables included HIT maturity scores. We included 5 independent variables (ie, ownership, chain status, location, number of beds, and occupancy rates). Unadjusted and adjusted cumulative odds ratios were calculated using regression models.

Results

Our sample (n=719) had a larger proportion of smaller facilities and a smaller proportion of larger facilities than the national nursing home population. Integrated clinical support technology had the lowest HIT maturity score compared to resident care HIT capabilities. The majority (n=486, 60.7%) of NHs report stage 3 or lower with limited capabilities to communicate about care delivery outside their facility. Larger NHs in metropolitan areas had higher odds of HIT maturity. The number of certified beds and NH location were significantly associated with HIT maturity stage while ownership, chain status, and occupancy rate were not.

Conclusions

NH structural disparities were recognized through differences in HIT maturity stage. Structural disparities in this sample appear most evident in HIT maturity, measuring integration of clinical support technologies for laboratory, pharmacy, and radiology services. Ongoing assessments of NH structural disparities is crucial given 1.35 million Americans receive care in these facilities annually. Leaders must be willing to promote equal opportunities across the spectrum of health care services to incentivize and enhance HIT adoption to balance structural disparities and improve resident outcomes.

An evaluation of telehealth expansion in U.S. nursing homes

OBJECTIVE

This research brief contains results from a national survey about telehealth use reported in a random sample of U.S. nursing homes.

METHODS AND MATERIALS

The sample includes nursing homes (N = 664) that completed surveys about information technology maturity, including telehealth use, beginning January 1, 2019, and ending August 4, 2020. A pre/post design was employed to examine differences in nursing home telehealth use for nursing homes completing surveys prior to and after telehealth expansion, on March 6, 2020. We calculated a cumulative telehealth score using survey data from 6 questions about extent of nursing home telehealth use (score range 0-42). We calculated proportions of nursing homes using telehealth and used logistic regression to look for differences in nursing homes based on organizational characteristics and odds ratios.

RESULTS

Significant relationships were found between nursing home characteristics and telehealth use, and specifically, larger metropolitan homes reported greater telehealth use. Ownership had little effect on telehealth use. Nursing homes postexpansion used telehealth applications for resident evaluation 11.24 times more (P < .01) than did nursing homes pre-expansion.

DISCUSSION

Administrators completing our survey reported a wide range of telehealth use, including approximately 16% having no telehealth use and 5% having the maximum amount of telehealth use. Mean telehealth use scores reported by the majority of these nursing homes is on the lower end of the range.

CONCLUSIONS

One solution for the current pandemic is to encourage the proliferation of telehealth with continued relaxed regulations, which can reduce isolation and preserve limited resources (eg, personal protective equipment) while maintaining proper distancing parameters.

Qualitative Validation of the Nursing Home IT Maturity Staging Model

The goal of the current study was to qualitatively explore issues of validity, specificity, and sensitivity regarding the nursing home (NH) information technology (IT) maturity survey and staging model. Participants who completed the NH IT maturity survey were recruited during pilot testing of the survey and staging model. Cognitive interviewing was used to collect qualitative data. Findings indicate the NH IT maturity survey and staging model is a straightforward and acceptable instrument. Every participant in our study agreed with the IT maturity stage assigned to their facility, based on their total score on the IT maturity survey. However, some participants were not sure how to answer some questions on the survey because they did not have in-depth knowledge of IT processes that took place outside of their NH facility and others experienced difficulty interpreting items because their NH facility was in a time of transition. The next step in development is quantitative psychometric testing and use of the instrument in a 3-year national study. [Journal of Gerontological Nursing, 46(7), 47-54].

Review of the Delphi method in library and information science research

Purpose

This article presents an introduction to the Delphi method and review of Delphi studies published in the literature of library and information science (LIS).

Design/methodology/approach

A review of Delphi studies published between the years of 1971 and 2019 is performed, using studies retrieved from the Library and Information Science Source database. A total of 122 articles were retrieved and evaluated based on the population studied, means of identifying experts, number of participants for each study round, type of Delphi, and type of findings.

Findings

General librarians (any type), academic librarians, and information science researchers are the most common populations in LIS Delphi studies. On average (middle 50 percent of studies), 14–36 experts are used in the first round of LIS Delphi studies (median n = 23). Employment in a specific role and publications in scholarly journals are the most common means of identifying experts. Variants of the e-Delphi (online survey/email) method are increasingly common, particularly in LIS Delphi studies that focus on general information science, rather than library, topics. Though LIS Delphi studies are relatively few in number, they have a consistent record of being published in some of the most prestigious LIS journals.

Originality/value

This paper provides an introduction to the Delphi method for LIS research and presents an overview of existing literature in LIS that utilizes the research method. No overview of this extent exists in the LIS literature, and, thus, this paper may serve as an important information source about the method for LIS researchers.

Correction to: Forecasting Content and Stage in a Nursing Home Information Technology Maturity Instrument Using a Delphi Method

The original article unfortunately contained a mistake.

Forecasting Content and Stage in a Nursing Home Information Technology Maturity Instrument Using a Delphi Method

Health information technology capabilities in some healthcare sectors, such as nursing homes, are not well understood because measures for information technology uptake have not been fully developed, tested, validated, or measured consistently. The paper provides a report of the development and testing of a new instrument measuring nursing home information technology maturity and stage of maturity. Methods incorporated a four round Delphi panel composed of 31 nursing home experts from across the nation who reported the highest levels of information technology sophistication in a separate national survey. Experts recommended 183 content items for 27 different content areas specifying the measure of information technology maturity. Additionally, experts ranked each of the 183 content items using an IT maturity instrument containing seven stages (stages 0-6) of information technology maturity. The majority of content items (40% (n = 74)) were associated with information technology maturity stage 4, corresponding to facilities with external connectivity capability. Over 11% of the content items were at the highest maturity stage (Stage 5 and 6). Content areas with content items at the highest stage of maturity are reflected in nursing homes that have technology available for residents or their representatives and used extensively in resident care. An instrument to assess nursing home IT maturity and stage of maturity has important implications for understanding health service delivery systems, regulatory efforts, patient safety and quality of care.