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DOI: 10.1055/s-0040-1722222
The Time is Now: Informatics Research Opportunities in Home Health Care
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Home health care (HHC) agencies face numerous challenges as they care for sicker patients due to shortened hospital stays and the discharge of patients before they are able to care for themselves.[1] The agencies face rehospitalization penalties,[1] [2] shortened episodes due to payment reform via the Patient-Driven Groupings Model,[3] and other pressures regarding quality and efficiency.[4] More recently, agencies face increased financial pressures due to reduced admissions, patient refusal of services, and costs for personal protective equipment due to the COVID-19 pandemic.[5] [6]
These challenges call for the productivity and quality gains offered by health information technology (HIT).[7] [8] While over 11,800 Medicare-certified HHC agencies[9] provide valued care across the United States, HHC, and other post-acute care settings were unfortunately omitted from Meaningful Use regulations requiring basic HIT functionality.[10] Thus, progress in supporting smooth information transfer and associated decision support lag behind acute and ambulatory care. While the Office of the National Coordinator for Health Information Technology initially funded longitudinal care coordination and electronic health information exchange (HIE) standards development for HHC, the funding was discontinued to address national health information network-to-network exchange.[11] This is unfortunate because the networks rarely include HHC agencies.
Current applications that are integrated into hospital electronic health record systems (EHRs) cannot be repurposed for HHC due to the unique home environment. Unlike in acute care settings, HHC clinicians operate independently under physician orders, to function effectively they need information to make specific decisions while in the home, and they often lack stable access to the internet.
The purpose of this editorial is to highlight for the health informatics community specific HHC informatics challenges to encourage more HIT development in the fast growing health care sector caring for disabled and vulnerable aging populations. Current HHC point-of-care EHR functionality tends to focus on the documentation of clinical data for reimbursement and compliance, requiring extensive (sometimes duplicative) data entry burdens. HIT solutions are needed to enhance data access, data processing and analysis, and information representation to increase efficiency, accuracy, and effectiveness. Although some state-of-the-art HHC HIT systems may enable interoperability of demographics and medication lists from hospital EHRs,[12] or documentation using clinical guidelines, these capabilities are not universal and do not address the range of capabilities needed.
The scenario in [Table 1] illustrates the informatics challenges present during the transition and admission process from acute to HHC and to set the stage for discussion regarding needed HIT solutions. Then the authors discuss major HIT challenges at the health care system level (interoperability and data standardization), at the HHC agency level (data analytics), at the clinician level (workflow and human factors), and at the society level (patient access and privacy).
Abbreviations: HHC, home health care; HIT, health information technology; EHR, electronic health record; OASIS, Outcome and Assessment Information Data Set.
Note: Fictional patient names used.
Protection of Human and Animal Subjects
No human subjects were involved in the project.
Publication History
Received: 25 August 2020
Accepted: 21 November 2020
Article published online:
17 February 2021
© 2021. Thieme. All rights reserved.
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References
- 1 Barnett ML, Grabowski DC, Mehrotra A. Home-to-home time — Measuring what matters to patients and payers. N Engl J Med 2017; 377 (01) 4-6
- 2 The Medicare Payment Advisory Commission. Report to the congress: Medicare payment policy. Washington, DC: MedPAC; 2019
- 3 Medicare Cf, Services M. Patient-Driven Groupings Model. Washington, DC: Centers for Medicare and Medicaid Services; 2019
- 4 Centers for Medicare and Medicaid Services. Patient-driven groupings model. Accessed 2018 at: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-payment/HomeHealthPPS/Downloads/Overview-of-the-Patient-Driven-Groupings-Model.pdf
- 5 National Association for Home Care & Hospice. National study shows home health care is in a fragile state. 2020 . Accessed October 5, 2020 at: https://www.nahc.org/wp-content/uploads/2020/03/National-survey-shows-home-health-care-on-the-frontlines-of-covid-19-and-continues-to-be-in-a-fragile-financial-state.pdf
- 6 Jones CD, Bowles KH. Emerging challenges and opportunities for home health care in the time of COVID-19. J Am Med Dir Assoc 2020; 21 (11) 1517-1518
- 7 Dowding D, Merrill JA, Barrón Y, Onorato N, Jonas K, Russell D. Usability evaluation of a dashboard for home care nurses. Comput Inform Nurs 2019; 37 (01) 11-19
- 8 Yang Y, Bass EJ, Bowles KH, Sockolow PS. Impact of home care admission nurses' goals on electronic health record documentation strategies at the point of care. Comput Inform Nurs 2019; 37 (01) 39-46
- 9 Medicare Payment Advisory Commission. Home health care services. Accessed 2019 at: http://www.medpac.gov/docs/default-source/reports/mar20_medpac_ch9_sec.pdf
- 10 Sockolow PS, Adelsberger M, Bossone C, Bowles KH. Identifying certification criteria for home care electronic health record meaningful use. Paper presented at: Improving Health: Informatics and IT Changing the World: AMIA Annu Symp Proc Washington, D.C. Accessed 2011 at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3243187/
- 11 Health IT. Moving beyond closed networks: an update on trusted exchange of health information. Accessed April 19, 2019 at: https://www.healthit.gov/buzz-blog/interoperability/moving-beyond-closed-networks-an-update-on-trusted-exchange-of-health-information
- 12 Champion CL, Sockolow PS, Bowles KH. et al. Getting to complete and accurate medication lists during the transition to home health care. J Am Med Dir Assoc 2020; DOI: 10.1016/j.jamda.2020.06.024.
- 13 Sockolow PSBK, Bowles KH, Wojciechowicz C, Bass EJ. Incorporating home healthcare nurses' admission information needs to inform data standards. J Am Med Inform Assoc 2020; 27 (08) 1278-1286
- 14 Renner SA. A community of interest approach to data interoperability. Accessed June 1, 2020 at: https://www.mitre.org/sites/default/files/pdf/renner_community.pdf
- 15 Samal L, Dykes PC, Greenberg JO. et al. Care coordination gaps due to lack of interoperability in the United States: a qualitative study and literature review. BMC Health Serv Res 2016; 16 (143) 143
- 16 Centers for Medicare and Medicaid. 42 CFR Parts 406, 407, 422, 423, 431, 438, 457, 482, and 485. In: Services HaH. ed; 2020: 474 . Accessed 2020 at: https://www.cms.gov/files/document/cms-9115-f.pdf
- 17 FHIR Management Group. HL7 Fast healthcare interoperability resources specification (FHIR®), DSTU release 1. Accessed Oct 5, 2020 at: https://www.hl7.org/implement/standards/product_brief.cfm?product_id=343
- 18 Apache Software Foundation. PDFBox. Accessed 2019 at: https://pdfbox.apache.org/
- 19 Centers for Medicare and Medicaid. IMPACT act of 2014 data standardization & cross setting measures. Accessed January 21, 2019 at: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-of-2014-Data-Standardization-and-Cross-Setting-Measures.html
- 20 Røsstad T, Garåsen H, Steinsbekk A, Sletvold O, Grimsmo A. Development of a patient-centred care pathway across healthcare providers: a qualitative study. BMC Health Serv Res 2013; 13: 121
- 21 Hellesø R, Fagermoen MS. Cultural diversity between hospital and community nurses: implications for continuity of care. Int J Integr Care 2010; 10: e036
- 22 Martin KS. The Omaha System: A key to practice, documentation, and information management. 2nd ed.. Omaha, NE: Health Connections Press; 2005
- 23 Bowles KH. Application of the Omaha System in acute care. Res Nurs Health 2000; 23 (02) 93-105
- 24 Centers for Medicare and Medicaid Services. Outcome and Assessment Information Set. Accessed May 23, 2020 at: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HomeHealthQualityInits/HHQIOASISUserManual
- 25 Osakwe ZT, Larson E, Agrawal M, Shang J. Assessment of activity of daily living among older adult patients in home healthcare and skilled nursing facilities: an integrative review. Home Healthc Now 2017; 35 (05) 258-267
- 26 Deb P, Murtaugh CM, Bowles KH. et al. Does early follow-up improve the outcomes of sepsis survivors discharged to home health care?. Med Care 2019; 57 (08) 633-640
- 27 O'Connor M, Hanlon A, Naylor MD, Bowles KH. The impact of home health length of stay and number of skilled nursing visits on hospitalization among Medicare-reimbursed skilled home health beneficiaries. Res Nurs Health 2015; 38 (04) 257-267
- 28 Bick I, Dowding D. Hospitalization risk factors of older cohorts of home health care patients: a systematic review. Home Health Care Serv Q 2019; 38 (03) 111-152
- 29 Topaz M, Woo K, Ryvicker M, Zolnoori M, Cato K. Home healthcare clinical notes predict patient hospitalization and emergency department visits. Nurs Res 2020; 69 (06) 448-454
- 30 Kreimeyer K, Foster M, Pandey A. et al. Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform 2017; 73: 14-29
- 31 Topaz M, Murga L, Gaddis KM. et al. Mining fall-related information in clinical notes: comparison of rule-based and novel word embedding-based machine learning approaches. J Biomed Inform 2019; 90: 103103
- 32 Bjarnadottir RI, Bockting W, Yoon S, Dowding DW. Nurse documentation of sexual orientation and gender identity in home healthcare: A text mining study. Comput Inform Nurs 2019; 37 (04) 213-221
- 33 Press MJ, Gerber LM, Peng TR. et al. Postdischarge communication between home health nurses and physicians: measurement, quality, and outcomes. J Am Geriatr Soc 2015; 63 (07) 1299-1305
- 34 Health IT. Clinical decision support: what is clinical decision support (CDS)?. Accessed April 10, 2018 at: https://www.healthit.gov/topic/safety/clinical-decision-support
- 35 Garg AX, Adhikari NK, McDonald H. et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005; 293 (10) 1223-1238
- 36 Topaz M, Trifilio M, Maloney D, Bar-Bachar O, Bowles KH. Improving patient prioritization during hospital-homecare transition: a pilot study of a clinical decision support tool. Res Nurs Health 2018; 41 (05) 440-447
- 37 Koru G, Parameshwarappa P, Alhuwail D, Aifan A. Facilitating focused process improvement efforts in home health agencies to improve utilization outcomes effectively and efficiently. Home Health Care Manage Pract 2018; 30 (03) 122-129
- 38 Nilsson L, Fagerström C. Decision-makers and mediators in a home healthcare digitisation process: nurses' experiences of implementation and use of a decision support system. Contemp Nurse 2018; 54 (4-5): 511-521
- 39 James G, Witten D, Hastie T, Tibshirani R. An Introduction to Statistical Learning: With Applications in R. New York: Springer Science+Business Media; 2013
- 40 Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst 2014; 2: 3
- 41 Uchidiuno U, Koru G, Parameshwarappa P, Alhuwail D. Outstanding practice-poster: leveraging data analytics to investigate the relationship between patient demographic and falls in home care. Comput Inform Nurs 2016; 34 (10) 435
- 42 Fletcher PC, Hirdes JP. Restriction in activity associated with fear of falling among community-based seniors using home care services. Age Ageing 2004; 33 (03) 273-279
- 43 Russell D, Baik D, Jordan L. et al. Factors associated with live discharge of heart failure patients from hospice. JACC Heart Fail 2019; 7 (07) 550-557
- 44 Bose E, Maganti S, Bowles KH, Brueshoff BL, Monsen KA. Machine learning methods for identifying critical data elements in nursing documentation. Nurs Res 2019; 68 (01) 65-72
- 45 Lo Y, Lynch SF, Urbanowicz RJ. et al. Using machine learning on home health care assessments to predict fall risk. Stud Health Technol Inform 2019; 264: 684-688
- 46 Welch HG, Wennberg DE, Welch WP. The use of Medicare home health care services. N Engl J Med 1996; 335 (05) 324-329
- 47 Bowles KH, Ratcliffe SJ, Holmes JH. et al. Using a decision support algorithm for referrals to post-acute care. J Am Med Dir Assoc 2019; 20 (04) 408-413
- 48 Jones A, Costa AP, Pesevski A, McNicholas PD. Predicting hospital and emergency department utilization among community-dwelling older adults: Statistical and machine learning approaches. PLoS One 2018; 13 (11) e0206662
- 49 National Association for Home Care & Hospice. CMS issues proposed rule: 2016 home health payment rates and value-based purchasing pilot. Accessed 2015 at: https://homecarenh.org/wp-content/uploads/2015/07/CMS-ISSUES-PROPOSED-RULE.pdf
- 50 Henriksen K, Joseph A, Zayas-Cabán T. The human factors of home health care: a conceptual model for examining safety and quality concerns. J Patient Saf 2009; 5 (04) 229-236
- 51 National Research Council. Health Care Comes Home: The Human Factors. Washington, DC: The National Academies Press; 2011
- 52 IOM Committee on Patient Safety and Health Information Technology. Health IT and Patient Safety: Building Safer Systems for Better Care. Washington, D.C.: National Academies Press; 2012
- 53 National Research Council. The Role of Human Factors in Home Health Care: Workshop Summary. Washington, D.C.: The National Academies Press; 2010
- 54 Karsh BT. Beyond usability: designing effective technology implementation systems to promote patient safety. Qual Saf Health Care 2004; 13 (05) 388-394
- 55 Devoge JMBE, Bass EJ, Sledd RM, Borowitz SM, Waggoner-Fountain L. collaborating with physicians to redesign a sign-out tool: an iterative, multifaceted approach with users - even busy ones - can yield a satisfying and efficient product. Ergon Des 2009; 17 (01) 20-28
- 56 Abbate AJBE. A formal methods approach to semiotic engineering. Int J Hum Comput Stud 2018; 115: 19
- 57 Sockolow PS, Bass E, Bowles KH, Holmberg A, Yang Y, Potashnik S. Data visualization of home care admission nurses' decision-making. Paper presented at: Proceedings of the AMIA 2017 Annual Symposium; Precision Informatics for Health: The Right Informatics for the Right Person at the Right Time. Washington D.C. Accessed 2017 at: https://pubmed.ncbi.nlm.nih.gov/29854230/
- 58 International Organization for Standardization. ISO 9241–210:2010 Ergonomics of human-system interaction–part 210: human-centred design for interactive systems. In: 1–32. Accessed 2010 at: https://infostore.saiglobal.com/preview/is/en/2010/i.s.eniso9241-210-2010.pdf?sku=1441363
- 59 Zahabi M, Kaber DB, Swangnetr M. Usability and safety in electronic medical records interface design: a review of recent literature and guideline formulation. Hum Factors 2015; 57 (05) 805-834
- 60 Association for the Advancement of Medical Instrumentation. ANSI/AAMI HE75:2009/(R): Human factors engineering-design of medical devices. Accessed 2018 at: https://www.fda.gov/media/80877/download2010
- 61 Food and Drug Administration. Applying human factors and usability engineering to medical devices: guidance for industry and food and drug administration. Accessed 2011 at: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/applying-human-factors-and-usability-engineering-medical-devices
- 62 Kotz D, Gunter CA, Kumar S, Weiner JP. Privacy and security in mobile health: a research agenda. Computer (Long Beach Calif) 2016; 49 (06) 22-30
- 63 Koru G, Alhuwail D, Topaz M, Norcio AF, Mills ME. Investigating the challenges and opportunities in home health care to facilitate effective information technology adoption. J Am Med Dir Assoc 2016; 17 (01) 53-58
- 64 Cavoukian A, Fisher A, Killen S. Remote home health care technologies: How to ensure privacy? Build it in: privacy by design. Ident Inform Soc 2010; 3 (02) 363-378
- 65 Kotz D, Avancha S, Baxi A. A privacy framework for mobile health and home-care systems. SPIMACS '09 Proceedings of the first ACM workshop on security and privacy in medical and home-care systems. In: 1–12. Accessed 2010 at: https://dl.acm.org/doi/10.1145/1655084.1655086
- 66 Casamba EMR Hit by Cyber Attack. 2019 . Accessed February 10, 2020 at: https://www.homecaremag.com/news/casamba-emr-hit-cyber-attack
- 67 Rowan T. Casamba Clients Respectfully Disagree. Accessed 2019 at: http://homecaretechreport.com/article/2949
- 68 El Emam K, Arbuckle L, Koru G. et al. De-identification methods for open health data: the case of the heritage health prize claims dataset. J Med Internet Res 2012; 14 (01) e33