Evidence map›Paper›PMID 35998030›Full record

ArticleJMIR aging2022

Examining Structural Disparities in US Nursing Homes: National Survey of Health Information Technology Maturity.

Gregory L Alexander, Jianfang Liu, Kimberly R Powell, Patricia W Stone

Abstract read
In one paragraph

Article in JMIR aging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Gregory L Alexander *School of Nursing, Columbia University, New York, NY, United States.ORCID https://orcid.org/0000-0003-4500-8797
Jianfang Liu *School of Nursing, Columbia University, New York, NY, United States.ORCID https://orcid.org/0000-0003-3546-0800
Kimberly R Powell *Sinclair School of Nursing, University of Missouri, Jefferson City, MO, United States.ORCID https://orcid.org/0000-0002-6144-1438
Patricia W Stone *School of Nursing, Columbia University, New York, NY, United States.ORCID https://orcid.org/0000-0003-4098-8853

Funding

A National Report of Nursing Home Quality Measures and Information TechnologyR01HS022497 · AHRQ · UNIVERSITY OF MISSOURI-COLUMBIA · PI ALEXANDER, GREGORY L. · 2013 to 2021
$2.7M
AHRQ HHS R01 HS022497
6 · The paper itself

Abstract

backgroundThere 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.

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

methodsNHs (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.

resultsOur 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.

conclusionsNH 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.

Indexed as

administrationcarecare providersclinical supportdata sharingelectronic dataelectronic health recordhealth information technologynursing homesnursing informaticspolicyquality of careresidentstructural disparity

Identifiers

PMID35998030
PMCPMC9449826

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.