Association between 30-day readmission rates and health information technology capabilities in US hospitals.
Cross-Sectional Studies
Electronic Health Records
/ statistics & numerical data
Hospital Bed Capacity
Hospitals
/ statistics & numerical data
Humans
Medical Informatics
/ statistics & numerical data
Patient Access to Records
/ statistics & numerical data
Patient Participation
/ statistics & numerical data
Patient Readmission
/ statistics & numerical data
Quality Indicators, Health Care
/ statistics & numerical data
Residence Characteristics
Retrospective Studies
United States
Journal
Medicine
ISSN: 1536-5964
Titre abrégé: Medicine (Baltimore)
Pays: United States
ID NLM: 2985248R
Informations de publication
Date de publication:
26 Feb 2021
26 Feb 2021
Historique:
received:
06
08
2020
accepted:
25
01
2021
entrez:
5
3
2021
pubmed:
6
3
2021
medline:
17
3
2021
Statut:
ppublish
Résumé
Health information technology (IT) is often proposed as a solution to fragmentation of care, and has been hypothesized to reduce readmission risk through better information flow. However, there are numerous distinct health IT capabilities, and it is unclear which, if any, are associated with lower readmission risk.To identify the specific health IT capabilities adopted by hospitals that are associated with hospital-level risk-standardized readmission rates (RSRRs) through path analyses using structural equation modeling.This STROBE-compliant retrospective cross-sectional study included non-federal U.S. acute care hospitals, based on their adoption of specific types of health IT capabilities self-reported in a 2013 American Hospital Association IT survey as independent variables. The outcome measure included the 2014 RSRRs reported on Hospital Compare website.A 54-indicator 7-factor structure of hospital health IT capabilities was identified by exploratory factor analysis, and corroborated by confirmatory factor analysis. Subsequent path analysis using Structural equation modeling revealed that a one-point increase in the hospital adoption of patient engagement capability latent scores (median path coefficient ß = -0.086; 95% Confidence Interval, -0.162 to -0.008), including functionalities like direct access to the electronic health records, would generally lead to a decrease in RSRRs by 0.086%. However, computerized hospital discharge and information exchange capabilities with other inpatient and outpatient providers were not associated with readmission rates.These findings suggest that improving patient access to and use of their electronic health records may be helpful in improving hospital performance on readmission; however, computerized hospital discharge and information exchange among clinicians did not seem as beneficial - perhaps because of the quality or timeliness of information transmitted. Future research should use more recent data to study, not just adoption of health IT capabilities, but also whether their usage is associated with lower readmission risk. Understanding which capabilities impact readmission risk can help policymakers and clinical stakeholders better focus their scarce resources as they invest in health IT to improve care delivery.
Identifiants
pubmed: 33663091
doi: 10.1097/MD.0000000000024755
pii: 00005792-202102260-00054
pmc: PMC7909153
doi:
Types de publication
Journal Article
Observational Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
e24755Subventions
Organisme : AHRQ HHS
ID : R01 HS022882
Pays : United States
Organisme : Agency for Healthcare Research and Quality
ID : R01HS022882
Informations de copyright
Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.
Déclaration de conflit d'intérêts
The authors have no conflicts of interest to disclose.
Références
Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006;144:742–52.
Gold M, McLaughlin C. Assessing HITECH Implementation and Lessons: 5 Years Later. Milbank Q 2016;94:654–87.
Hessels A, Flynn L, Cimiotti JP, et al. Impact of heath information technology on the quality of patient care. On-line J Nurs informatics 2015. 19 http://www.himss.org/impact-heath-information-technology-quality-patient-care
Kripalani S, Jackson AT, Schnipper JL, et al. Promoting effective transitions of care at hospital discharge: A review of key issues for hospitalists. J Hosp Med 2007;2:314–23.
Kripalani S, Theobald CN, Anctil B, et al. Reducing Hospital Readmission Rates: Current Strategies and Future Directions. Annu Rev Med 2014;65:471–85.
Alper E, O’Malley TA, Greenwald J. Hospital discharge and readmission. UpToDate 2019; https://www.uptodate.com/contents/search?search=Hospital%20Discharge%20and%20Readmission&x=0&y=0https://www.uptodate.com/contents/search?search=Hospital%20Discharge%20and%20Readmission&x=0&y=0 . Accessed February 3, 2021.
Al-Damluji MS, Dzara K, Hodshon B, et al. Association of discharge summary quality with readmission risk for patients hospitalized with heart failure exacerbation. Circ Cardiovasc Qual Outcomes 2015;8:109–11.
Horwitz LI, Moriarty JP, Chen C, et al. Quality of discharge practices and patient understanding at an academic medical center. JAMA Intern Med 2013;173:1715–22.
O’Leary KJ, Liebovitz DM, Feinglass J, et al. Creating a better discharge summary: Improvement in quality and timeliness using an electronic discharge summary. J Hosp Med 2009;4:219–25.
Ammenwerth E, Schnell-Inderst P, Machan C, et al. The Effect of Electronic Prescribing on Medication Errors and Adverse Drug Events: A Systematic Review. Journal of the American Medical Informatics Association 2008;15:585–600.
Kaushal R, Kern LM, Barrón Y, et al. Electronic prescribing improves medication safety in community-based office practices. J Gen Intern Med 2010;25:530–6.
Sharma AE, Rivadeneira NA, Barr-Walker J, et al. Patient Engagement In Health Care Safety: An Overview Of Mixed-Quality Evidence. Health Aff 2018;37:1813–20.
Norton PT, Rodriguez HP, Shortell SM, et al. Organizational influences on healthcare system adoption and use of advanced health information technology capabilities. Am J Manag Care 2019;25:e21–5.
Mehta RL, Baxendale B, Roth K, et al. Assessing the impact of the introduction of an electronic hospital discharge system on the completeness and timeliness of discharge communication: a before and after study. BMC Health Serv Res 2017;17:doi:10.1186/s12913-017-2579-3.
Buntin MB, Burke MF, Hoaglin MC, et al. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff 2011;30:464–71.
Jones SS, Friedberg MW, Schneider EC. Health information exchange, Health Information Technology use, and hospital readmission rates. AMIA Annu Symp Proc 2011;2011:644–53.
Ben-Assuli O, Shabtai I, Leshno M. The impact of EHR and HIE on reducing avoidable admissions: controlling main differential diagnoses. BMC Med Inform Decis Mak 2013;13: doi:10.1186/1472-6947-13-49.
doi: 10.1186/1472-6947-13-49
Jones SS, Rudin RS, Perry T, et al. Health information technology: an updated systematic review with a focus on meaningful use. Ann Intern Med 2014;106:48–54.
Yanamadala S, Morrison D, Curtin C, et al. Electronic health records and quality of care an observational study modeling impact on mortality, readmissions, and complications. In: Medicine (United States) 2016;95:e3332.
Ryan AM, Krinsky S, Adler-Milstein J, et al. Association between hospitals’ engagement in value-based reforms and readmission reduction in the hospital readmission reduction program. JAMA Intern Med 2017;177:862–8.
Kruse CS, Beane A. Health information technology continues to show positive effect on medical outcomes: systematic review. J Med Internet Res 2018;20:e41doi:10.2196/jmir.8793.
doi: 10.2196/jmir.8793
Centers for Medicare & Medicaid Services. 2016 Hospital Compare downloadable database. Centers for Medicare & Medicaid Services. Available at: https://data.cms.gov/provider-data/sites/default/files/archive/Hospitals/2016/hos_revised_flatfiles_archive_05_2016.zip . Accessed July 19, 2019.
American Hospital Association IT Supplement Survey. 2013 American Hospital Association IT Supplement Survey. AHA annual survey information technology supplement: Survey questionnaire. https://www.ahadataviewer.com/Global/IT surveys/2013 AHA Annual Survey IT Supplement Survey.pdf. Published 2013. Accessed July 19, 2019.
American Hospital. Association IT Supplement Survey. 2013 AHA annual survey information technology supplement: Custom Database. Am Hosp Assoc IT Suppl Surv Cust Database 2013.
Horwitz LI, Partovian C, Lin Z, et al. Development and use of an administrative claims measure for profiling hospital-wide performance on 30-day unplanned readmission. Ann Intern Med 2014. 161(10_Supplement):S66. doi:10.7326/m13-3000.
doi: 10.7326/m13-3000
Rubin BD. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley; 1987.
Everitt BS. Multivariate analysis: the need for data, and other problems. Br J Psychiatry 1975;126:237–40.
Brown, Dean J. Choosing the right type of rotation in PCA and EFA. JALT Test Eval SIG Newsl 2009;13:20–5.
Satorra A, Bentler PM. Corrections to test statistics and standard errors in covariance structure analysis. In: Latent Variables Analysis: Applications for Developmental Research. 1994;Thousand Oaks, CA: SAGE Publications Ltd, 399–419.
Froehle CM, Roth AV. New measurement scales for evaluating perceptions of the technology-mediated customer service experience. J Oper Manag 2004;22:1–21.
Schumacker RE, Lomax RG. A Beguiner's guide to structural equation modeling 3rd edition 2010. 1–510.
Mak BL, Sockel H. A confirmatory factor analysis of IS employee motivation and retention. Inf Manag 2001;38:265–76.
Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model 1999;6:1–55.
Hu LT, Bentler PM. Fit Indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychol Methods 1998;3:424–53.
Koehler BE, Richter KM, Youngblood L, et al. Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med 2009;4:211.
Irizarry T, Shoemake J, Nilsen ML, et al. Patient portals as a tool for health care engagement: a mixed-method study of older adults with varying levels of health literacy and prior patient portal use. J Med Internet Res 2017;19:e99doi:10.2196/jmir.7099.
doi: 10.2196/jmir.7099
Institute of MedicineCrossing the quality chasm: a new health system for the 21st century - institute of medicine 2001. 1–360. doi:10.17226/10027.
doi: 10.17226/10027
Hibbard JH, Greene J. What the evidence shows about patient activation: better health outcomes and care experiences; fewer data on costs. Health Aff 2013;32:207–14.
Kruse CS, Bolton K, Freriks G. The effect of patient portals on quality outcomes and its implications to meaningful use: a systematic review. J Med Internet Res 2015;17:e44doi:10.2196/jmir.3171.
doi: 10.2196/jmir.3171
Griffin A, Skinner A, Thornhill J, et al. Patient Portals: Who uses them? What features do they use? And do they reduce hospital readmissions? Appl Clin Inform 2016;7:489–501.
Kaplan B, Brennan PF. Consumer informatics supporting patients as co-producers of quality. J Am Med Informatics Assoc 2001;8:309–31.
Reed ME, Huang J, Brand RJ, et al. Patients with complex chronic conditions: health care use and clinical events associated with access to a patient portal. PLoS One 2019;14:e0217636doi:10.1371/journal.pone.0217636.
doi: 10.1371/journal.pone.0217636
Holmgren AJ, Apathy NC, Adler-Milstein J. Barriers to hospital electronic public health reporting and implications for the COVID-19 pandemic. J Am Med Informatics Assoc 2020;27:1–4.
Dixon BE, Vreeman DJ, Grannis SJ. The long road to semantic interoperability in support of public health: experiences from two states. J Biomed Inform 2014;49:3–8.
Bao C, Bardhan IR, Singh H, et al. Patient-provider engagement and its impact on health outcomes: a longitudinal study of patient portal use. MIS Q Manag Inf Syst 2020;44:699–723.
Ahern DK, Woods SS, Lightowler MC, et al. Promise of and potential for patient-facing technologies to enable meaningful use. Am J Prev Med 2011;40: (5, Supplement 2): S162–72.
Lin SC, Lyles CR, Sarkar U, et al. Are Patients Electronically Accessing Their Medical Records? Evidence From National Hospital Data. Health Aff 2019;38:1850–7.