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Appl Clin Inform 2021; 12(01): 100-106
DOI: 10.1055/s-0040-1722222
Invited Editorial

The Time is Now: Informatics Research Opportunities in Home Health Care

Paulina S. Sockolow
1   College of Nursing and Health Professions, Drexel University, Philadelphia, Pennsylvania, United States
,
Kathryn H. Bowles
2   Department of Biobehavioral Health Science, NewCourtland Center for Transitions and Health, University of Pennsylvania School of Nursing, Philadelphia, Pennsylvania, United States
3   Center for Home Care Policy and Research, Visiting Nurse Service of New York, New York, United States
,
Maxim Topaz
4   Columbia University School of Nursing, Columbia University Data Science Institute, Visiting Nurse Service of New York, New York, United States
,
Gunes Koru
5   Department of Information Systems, University of Maryland Baltimore County, Baltimore, Maryland, United States
,
Ragnhild Hellesø
6   Department of Nursing Science, Institute of Health and Society, University of Oslo, Oslo, Norway
,
Melissa O'Connor
7   M. Louise Fitzpatrick College of Nursing, Villanova University, Villanova, Pennsylvania, United States
,
Ellen J. Bass
8   College of Nursing and Health Professions, College of Computing and Informatics, Drexel University, Philadelphia, Pennsylvania, United States
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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).

Table 1

Scenario of the informatics challenges during the transition and admission process from acute care to home health care

Mrs. Jones, 70-year-old, is discharged from hospital to home where she lives alone. Like most older adults, she has multiple chronic conditions and takes more than 12 prescribed and over the counter medications. Upon hospital discharge, she is given a paper discharge summary. The HHC agency—lacking interoperability with the hospital—receives referral documents consisting of 40 pages without a standardized format, content, or computability. With this information and without associated decision support, agency staff tries to identify high-risk patients to be scheduled for admission visits within 24 to 48 hours by the nurse.

To prepare for the first visit, and in the anticipation of unreliable internet service in the patient home, the nurse works at home the evening or morning before the visit to peruse through the 40 pages of referral documents. Lacking guidance from a comprehensive problem list or decision support integrated into the EHR, the nurse tries to discern from among the many chronic conditions and acute exacerbations which problems are active, resolved, or potential to determine the focus of the HHC episode.

The nurse enters the home, hopeful to find an uncluttered place to sit, and place her laptop. Because of the patient's fatigue from her hospital stay and self-care demands, the nurse has limited time (usually 1 hour) to:

 (1) Gather the information to answer the Centers for Medicare and Medicaid Services mandated 90 question admission assessment (OASIS)

 (2) Conduct a physical assessment and assessment of the home environment

 (3) Find the prescription and over the counter medications in the home and reconcile them with the hospital discharge medication list located in different documents

 (4) Call the physician or pharmacy to resolve medication discrepancies and potential adverse effects

 (5) Assess Mrs. Jones' medication self-management capability

 (6) Determine which of Mrs. Jones' many problems spread across different documents and identified during the assessment to include on the HHC plan of care

 (7) Recommend to the HHC care team which, if any, other HHC services Mrs. Jones needs (e.g., physical therapy)

 (8) Check the current and trended vital sign data on Mrs. Jones' home blood pressure monitor

 (9) Educate the patient and/or caregiver in how to access current and trended vital sign data

 (10) Elicit from Mrs. Jones her upcoming medical appointments, negotiate with her the timing and frequency of follow-up nursing visits, and schedule these appointments

 (11) Document these aspects of the admission in the EHR.

 The nurse leaves to see five more patients for follow-up visits scheduled for that day, and Mrs. Jones tries to get some rest.

At the end of the day, the nurse, now at home with reliable internet access and remembering to check for a reply from the physician about the medication issue, finalizes the admission. Completing the documentation will entail transcription of information from hospital referral documents and duplicative data entry. Team members who will subsequently visit Mrs. Jones will now be able to see the plan of care and summary note.

The admission documentation will also be reviewed by additional agency staff to assure internal consistency for reimbursement, in lieu of clinical decision support, as the nurse documents. Further analysis of the data collected and noted by the nurse to proactively identify clinical trends or identify cohorts for care management is not possible as important data are stored as text, and the EHR lacks the needed HIT tools for synthesis and reporting.

The agency, seeking new knowledge while ensuring patient privacy, seeks to provide de-identified structured data (e.g., OASIS data) to researchers who use statistical or machine learning techniques to help them prioritize patient visits for the rest of the week.

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

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