Table of Contents


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Home Healthcare, Hospice, and Information Technology Innovations Conference

Innovations in Home Healthcare, Hospice, and Information Technology

A forum towards achieving evidence-based diffusion and implementation of innovations

Fri, Nov 3, 2017
Friday, Nov 3, 2017, Washington - D.C.
Proceedings of the Home Healthcare,
Hospice, and Information Technology
Conference
Volume 1
Washington DC
November 2014
Steering Committee
Kathryn Bowles PhD, RN, FAAN General Chair School
of Nursing University of Pennsylvania
George Demiris PhD, FACMI Program Committee
Chair School of Nursing & Biomedical and Health In-
formatics, School of Medicine University of Washington
Rupa Valdez Organizing Committee Chair Department
of Public Health Sciences University of Virginia School
of Medicine
Güneş Koru Organizing Committee Chair Department
of Information Systems University of Maryland Balti-
more County
Max Topaz International Relations Chair School of
Nursing University of Haifa
Dari Alhuwail Local Arrangements Chair Department
of Information Systems University of Maryland Balti-
more County
Shradha Aiyer Health IT Vendor Relationships Com-
mittee Chair Mobile Technology Solutions at Axxess
Ann Horton Provider and Government Relationships
Committee Chair Maryland National Capital Home
Care Association
Richard D. Brennan, Jr. NAHC Liaison National Asso-
ciation of Home Care and Hospice
Program Committee
Dari Alhuwail, University of Maryland Baltimore
County
Kathryn Bowles, University of Pennsylvania
John Cagle, University of Maryland Baltimore
George Demiris, University of Washington (chair)
Birthe Dinesen, Aalborg University
Angelica Herrera, University of Maryland Baltimore
County
Sabine Koch, Karolinska Institute
Güneş Koru, University of Maryland Baltimore County
Robert Lucero, Columbia University
Karen Marek, Arizona State University
Michael Marschollek, Medical School
Karen Monsen, University of Minnesota
Huong Nguyen, Kaiser Permanente Research
Anthony Norcio, University of Maryland Baltimore
County
Debra Oliver, University of Missouri
Guy Pare, HEC Montreal
Kavita Radhakrishnan, University of Texas at Austin
Paulina Sockolow, Drexel University
Max Topaz, University of Pennsylvania
Oleg Zaslavsky, University of Haifa
Editor: Güneş Koru, PhD
Typesetting: Pratik Tamakuwala and Ketan Patil
Sponsors:
2014 - i
Contents
1. Development and Implementation of a Predictive Model of Hospitalization Risk among Pediatric
Home Care Patients Daniel Kurowski, David Russell, Tasha Hamilton, Rocco Napoli, Robert Rosati . . . . . . . 1
2. Developing a Tool to Support Decisions on Patient Prioritization at Admission to Home Health Care
Maxim Topaz, Kathryn H. Bowles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3. Development of an eHealth Measures Compendium Bonnie Wakefield, Timothy Hogan, Carolyn Turvey,
Stephanie Shimada, Kim Nazi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
4. Personal Health Information Management in Older Adults: Use of Patient Portals Anne Turner, Katie
Osterhage, Andrea Hartzler, George Demiris . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
5. Toward Effective and Efficient Health IT Adoption in Home Healthcare: A Qualitative Investigation
of Maryland Home Health Agencies Gunes Koru, Dari AlHuwail, Maxim Topaz, Mary Etta Mills, Anthony
F. Norcio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
6. Sharing the Journey: Technology-Enhanced Transitional Palliative Care Diane E. Holland, Catherine E.
Vanderboom, Cory J.Ingram, Ann Marie Dose, Ellen Wild, Kathryn H. Bowles . . . . . . . . . . . . . . . . . . . . 8
7. Sustainability of Home Telepathy Programs: A Systematic Review Kavita Radhakrishnan, Bo Xie, Amy
Ellis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
8. Use and Satisfaction With Wearable Activity Trackers Among Community Dwelling Older People
Elizabeth Madigan, Chia Hua Lin, Mehran Mehregany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
9. The Electronic Collection and Presentation of Nocturnal Heart Failure Cubby L. Gardner, Harry B. Burke 14
10. Information Practices and Information Systems in Home Health Care: A Field-Study Ragnhild
Helleso, Merete Lyngstad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2014 - ii
H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
Development and Implementation of a Predictive
Model of Hospitalization Risk among Pediatric
Home Care Patients
Daniel Kurowski
1
, David Russell
1
, Tasha Hamilton
1
, Rocco Napoli
2
, Robert Rosati
3
P
redictive models of hospitalization risk can be used to identify patients who may benefit from more inten-
sive services and transitional care interventions. Previous work on predictive hospitalization risk models for
adult home care patients has been undertaken. The aim of this presentation is to describe the development
and implementation of a predictive model of hospitalization risk for a pediatric homecare population using
information collected at the start of home care. We examined a range of demographic and clinical characteristics including:
referral source, prior admission status, the presence of a complex chronic condition, self-rated general health, the use of
therapy equipment, and prescription medications.
Methods: We conducted a retrospective observational study of pediatric home care patients who were served by a large,
urban, not-for-profit home healthcare agency and who were admitted and discharged from 2008 to 2011 (N=14,216). In-
formation was obtained from patient intake, clinical assessment and medication history databases. We employed a logistic
regression model with backward selection to examine the demographic and clinical characteristics associated with hospitaliza-
tion within 60 days of admission to home care. Predicted probabilities were calculated from the model and used to estimate
each patient’s level of hospitalization risk. Concordance between predictive model scores and judgments of hospitalization
risk made via chart reviews by three clinical nurse specialists was evaluated using Cohen’s kappa statistic.
Results: A number of clinical factors were found to be significantly associated with hospitalization, including: the pres-
ence of a complex chronic condition, use of medical therapy equipment (oxygen, IV/infusion therapy, and enteral/parenteral
nutrition), fair or poor self-rated health, and prescriptions within certain therapeutic classes of medications (i.e. ulcer med-
ications, psychotropic medications, anticonvulsants, anti-nauseates, penicillins, hematinics, and sedative barbiturates). We
also identified service characteristics that were significantly associated with hospitalization risk, including: referral from the
hospital, a prior history of home care service, and enrollment in a managed care insurance plan. Patients with the highest
predicted level of risk had a hospitalization rate of 28.7% compared to a hospitalization rate of 1.2% among patients with
the lowest predicted level of risk. Results from the model validation indicated a moderate level of agreement between the
predictive model and the nurse specialist’s risk judgment (Cohen’s kappa=0.44); reasons for moderate agreement will be
reviewed in detail. Hospitalization risk scores for newly admitted patients are disseminated to supervisors once a week via
e-mail using an automated SAS program.
Discussion: Our findings suggest that information collected at the start of home care can be used to identify pediatric
patients who have a greater risk of hospitalization. In our population of pediatric patients, 63% of hospitalized home care
patients were discharged to the hospital within 14 days of admission to home care. Special attention must be paid to the
information technology (IT) requirements associated with moving data into an analytic warehouse that is capable of process-
ing and returning a risk score. IT applications that reduce the lag time between assessment and processing may be useful to
home care organizations.
Conclusion: The use of data-driven risk measures can be used to assist administrative staff in managing clinical resources
for pediatric home care patients, however, special attention should be made to ensure timely dissemination. Further research
is needed to evaluate whether strategies implemented to mitigate risk factors are effective in reducing hospitalization rates
among this population.
1
Visiting Nurse Service of New York
2
CenterLight Healthcare
3
VNA Health Group
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H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
Developing a Tool to Support Decisions on Patient
Prioritization at Admission to Home Health Care
Maxim Topaz
1, 2
, Kathryn H. Bowles
1,3
M
illions of Americans are discharged from hospitals to home health every year and about third of them return
to hospitals. A significant number of rehospitalizations (up to 60%) happen within the first two weeks of
services. Early targeted allocation of services for patients who need them the most, have the potential to
decrease readmissions. Unfortunately, there is only fragmented evidence on factors that should be used to
identify high-risk patients in home health. This study aimed to (1) identify factors associated with priority for the first home
health nursing visit and (2) to construct and validate a decision support tool for patient prioritization.
Methods: We recruited a geographically diverse convenience sample of nurses with expertise in care transitions and care
coordination to identify factors supporting home health care prioritization. This was a predictive study of home health visit
priority decisions made by 20 nurses for 519 older adults referred to home health. Variables included socio-demographics,
diagnosis, comorbid conditions, adverse events, medications, hospitalization in last 6 months, length of stay, learning ability,
self-rated health, depression, functional status, living arrangement, caregiver availability and ability and first home health
visit priority decision. A combination of data mining and logistic regression models was used to construct and validate the
final model.
Results: The model identified five factors associated with first home health visit priority. A cut point for decisions on
low/medium versus high priority was derived with a sensitivity of 80% and specificity of 57.9%, area under receiver operator
curve (ROC) 75.9%. Nurses were more likely to prioritize patients who had wounds (odds ratio [OR]=1.88), comorbid con-
dition of depression (OR=1.73), limitation in current toileting status (OR= 2.02), higher numbers of medications (increase
in OR for each medication =1.04) and comorbid conditions (increase in OR for each condition =1.04).
Discussion: This study developed one of the first clinical decision support tools for home health, the PREVENT- Priority
for Home Health Visit Tool. Further work is needed to improve the specificity and generalizability of the tool, implement an
electronic version and test its effects on patient outcomes.
1
University of Pennsylvania, School of Nursing
2
Brigham and WomenÕs health hospital
3
Visiting Nurse Service of New York
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H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
Development of an eHealth Measures Compendium
Bonnie Wakefield
1
, Timothy Hogan
1
, Carolyn Turvey
2
, Stephanie Shimada
1
, Kim Nazi
3
C
onsistent and well-validated metrics of design, efficiency, and improved communication are necessary to deter-
mine the true benefit of any eHealth intervention without which healthcare organizations cannot 1) calculate
return on investment of eHealth technology; 2) effectively address barriers to adoption that stem from these
metrics (i.e. usability, accessibility of a technology); or 3) accurately estimate the likelihood of adoption. The
goal of this project is to create a compendium of potential metrics that could be used in any study using eHealth interventions
and create a standardized array of recommended metrics that will support both eHealth operations and research.
Methods: Working with an experienced health sciences librarian, an extensive list of search terms were developed address-
ing platforms (e.g., cell phone, patient portal), measurement (e.g., performance measurement, survey development) and
functions (e.g., health information seeking). To date, major healthcare literature databases have been searched including
Scopus, Pubmed, Cumulative Index of Nursing and Allied Health Literaterue (CINAHL), Health and Psychosocial Instru-
ments (HAPI), and PsychInfo. To build this compendium effectively, the literature search will extend beyond a review of
the medical literature and include research from the IEEE (Institute of Electrical and Electronics Engineers) and ACM
(Association for Computing Machinery) digital libraries. Reliability estimates will be explored using the range of current
statistics available (e.g. internal consistency, test-retest reliability, alternate forms reliability) as will validity estimates (con-
tent validity, construct validity, predictive validity, discriminant validity). Each metric will be described using a uniform
format. A brief overview of the instrument’s development, scoring procedures, psychometric properties, key references on
the development and/or use of the instrument, and the actual scale (if available) will be included. The final compendium
will be searchable by key words (using MeSH terms) so each metric will be cross-indexed by the topic/construct covered, the
types of technology the metric addresses, and populations where the metric has been used. The database will also include
references to articles or abstracts on use of the metric. Finally, at the conclusion of the search and review, we will upload
our information to the Grid-Enabled Measures (GEM) database, sponsored by the National Cancer Institute.
Results: The study is in progress. We have developed and tested the review instrument using the uniform requirements
addressed above. To date, 15 instruments known to the investigators prior to the search have been reviewed. The search of
the healthcare literature resulted in 33,217 citations; approximately 70% have undergone a title/abstract review. Of those,
less than 1% describe an instrument and less than 4% describe use of an instrument that potentially could be included in
the compendium.
Discussion: Evaluation of eHealth is unique from evaluation of other interventions in three important ways that warrant
development of a unique compendium: 1) it must include evaluation of the technology platforms and functions in terms of
usability, functionality, and availability of the technology to target users; 2) eHealth applications are promoted to improve
efficiency and accessibility, but there are no uniform widely agreed upon metrics; and 3) eHealth interventions aim to improve
communication in one form or another, thus metrics are needed that quantify specifically the degree to which communication
is improved.
Conclusion: The results of this project will provide critical insights regarding existing eHealth measures and identify gaps
where new metrics are needed. The compendium can also inform future studies so that the results from multiple studies can
be compared and synthesized because they used the same handful of metrics. A white paper will be developed to provide a
critical synthesis and analysis of the current state of evaluation of eHealth in light of the strengths and weaknesses for each
of the domains covered.
1
VA Medical Center
2
VA University of Iowa
3
Veterans Health Administration
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H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
Personal Health Information Management in Older
Adults: Use of Patient Portals
Anne Turner
1
, Katie Osterhage
1
, Andrea Hartzler
1
, George Demiris
1
I
nformation technologies for assisting older adults in managing their health information (e.g., patient portals)
have not been widely adopted, in part because they are developed without knowledge of what older adults want
and their health information management practices. The purpose of our AHRQ-funded SOARING (Studying
Older Adults and Researching Information Needs and Goals) Project is to investigate the personal health
information management goals, activities and practices of older adults in a variety of living environments. Findings will be
used to inform the design of tools which are tailored to the needs and health information management practices of older
adults.
Methods: We conducted 39 in-depth interviews with older adults whom we recruited from adult residential centers, assisted
living, and independent homes and apartments. We used a purposeful recruitment strategy to ensure diverse representation
of age, gender, socio-economic status, and racial and ethnic background. Inclusion criteria included age 60 years or older,
ability to speak and write English, lack of severe cognitive impairments, and ability to provide informed consent. Interviews
consisted of standardized surveys regarding demographics, overall health, social networks and use of technology, in addition
to open-ended questions concerning how participants manage their personal health information. Interviews focused on health
and personal health information management in general and did not focus on the use of specific technologies. We audio-
recorded, transcribed, and analyzed interviews for qualitative themes.
Results: Participants described information management styles ranging from complex systems for tracking medication
use and clinical visits to simply discarding all but the most critical personal health information. Seventy two percent of
participants (28/39) reported using a computer at least 2-3 days per week and 56% of participants (22/39) reported using
a computer 6-7 days per week. The majority of computer users reported accessing the Internet. A significant number of
participants (8/39) mentioned their use of patient portals, defined as a secure Website through which patients can access a
personal health record and often certain information from an electronic health record.
1
The ages of those eight participants
ranged from 73 to 93. By and large, those participants live independently, are well-educated, and represent middle to higher
incomes. The majority of participants using patient portals said they felt positive about the specific portal they use. A
majority of the patient portals mentioned were implementations of Epic MyChart (Epic Systems Corp, Verona, WI).
Specific discussion about patient portals generally focused on their usefulness and ease of use. Several participants mentioned
they have greatly reduced their own personal record keeping of health information because they access this information
through the patient portal. One participant reported that they used a portal briefly, but stopped because of frustrations
with logging in.
Discussion: Many older adults are using computers and accessing the Internet. Despite reports of barriers to the use
personal health information technologies by older adults,
2–6
a surprising 20% of the older adults we interviewed use patient
portals to manage personal health information. This trend is encouraging for potential future adoption of patient portals by
older adults. Expanded research is needed to determine the general penetration of patient portals, factors that contribute to
portal use by older adults, and associations between use of patient portals and independent living.
Conclusion: Study findings highlight the value of patient portals as a platform to facilitate management of personal health
information and demonstrate their potential to help older adults maintain wellness and independence as well as to enhance
home care services in various residential settings.
References
1. Tang, PC, Ash, JS, Bates, DW, Overhage, JM, and Sands, DZ. Personal health records: definitions, benefits, and strate-
gies for overcoming barriers to adoption. Journal of the American Medical Informatics Association: JAMIA 2006;13:121–
126.
2. Lusignan, S de, Ross, P, Shifrin, M, Hercigonja-Szekeres, M, and Seroussi, B. A comparison of approaches to providing
patients access to summary care records across old and new europe: an exploration of facilitators and barriers to
implementation. Studies in Health Technology and Informatics 2013;192:397–401.
1
University of Washington
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3. Greenhalgh, T, Hinder, S, Stramer, K, Bratan, T, and Russell, J. Adoption, non-adoption, and abandonment of a
personal electronic health record: case study of HealthSpace. BMJ: British Medical Journal 2010;341:1091.
4. Pyper, C, Amery, J, Watson, M, and Crook, C. Access to electronic health records in primary care-a survey of patients’
views. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research 2004;10:SR17–22.
5. Kim, EH, Stolyar, A, Lober, WB, Herbaugh, AL, Shinstrom, SE, Zierler, BK, Soh, CB, and Kim, Y. Usage Patterns of
a Personal Health Record by Elderly and Disabled Users. AMIA Annual Symposium Proceedings 2007;2007:409–413.
6. Ruland, CM, Kresevic, D, and Lorensen, M. Including patient preferences in nurses’ assessment of older patients. Journal
of Clinical Nursing 1997;6:495–504.
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Toward Effective and Efficient Health IT Adoption
in Home Healthcare: A Qualitative Investigation of
Maryland Home Health Agencies
Güneş Koru
1
, Dari AlHuwail
1
, Maxim Topaz
2
, Mary Etta Mills
3
, Anthony F. Norcio
1
H
ealth information technology (HIT) becomes a critical tool in home healthcare as its utilization increases.
Compared to other types of healthcare providers, HIT adoption levels among home health agencies(HHAs)
have traditionally been lower.
1
Furthermore, various eligibility issues prevented HHAs from receiving finan-
cial incentives for adopting electronic health records (EHR),
2
which can be considered among essential HIT
systems. Most HHAs in the United States (US) are in a position to adopt HIT solutions in highly budget constrained
settings where it is crucial to achieve effective and efficient HIT adoption. In this context, effectiveness means creating the
maximum value possible with limited resources; efficiency means minimizing the overheads of HIT adoption. We conducted
a qualitative study to obtain rich contextual information strengthening the evidence base about the (i) HHAs’ challenges and
opportunities related to delivering care and conducting business,which should derive HIT adoption strategies and decisions to
achieve effectiveness (ii) contextual determinants of HIT adoption that should be managed to achieve efficiency by minimizing
overheads.
Methods: Semi-structured phone interviews were conducted with the executives and managers of thirteen Maryland HHAs.
Maximum variation was used in recruitment by considering the HHAs’ size, organization type, business model, geographical
areas served, and age. For each recruited HHA, one interview was conducted involving either two participants, one knowl-
edgeable in HIT and the other in home care, or involving only one participant knowledgeable in both areas. The topical
areas were based on (i) a number of established systems analysis techniques such as problem analysis, duration analysis,
activity-based costing, outcome analysis,and technology analysis to document the HHAs’ challenges and opportunities (ii)
the constructs in the Rogers’ diffusion theory
3
to uncover the contextual determinants of adoption. The interview transcripts
provided the raw data analyzed using the Framework method.
4–7
The analysis of qualitative data included constructing an
index, open coding, summarizing and sorting, and eliciting descriptive and explanatory accounts.
Results: (i) Coordinating clinical and administrative work flows was stated as an important challenge. Complying with
the strict and changing Federal rules for reimbursements, therapy assessments, and physician approvals was described as
excessively time consuming and costly, particularly for smaller HHAs. It was reported that HHAs use telephone and fax as
the primary means of health information exchange (HIE). Most participants complained about not having adequate access
to patients’ medical history during admissions. Hiring and training qualified clinicians was considered to be a challenge for
HHAs. Some participants noted that the scheduling and training difficulties increase greatly as the number of part-time
employees increase. Educating and training patients and caregivers was found to improve outcomes, but it required overcom-
ing cultural, educational, and agerelated barriers. Smaller HHAs experienced significant difficulties with getting referrals.
(ii) Most HHAs lacked defined processes for analysing their HIT requirements driven by their actual improvement needs,
evaluating alternative HIT solutions, and making HIT adoption decisions. Perceived complexity of using HIT was mentioned
as a challenge but the HHAs were able to train most clinicians successfully if their training budgets allowed. Still, the partic-
ipants mentioned that using EHR at patients’ home presented usability issues which sometimes detracted from the quality
of interaction. The participants perceive HIT to be useful but they said the opinions varied among their clinicians. While
larger HHAs customized HIT solutions to a certain extent, most HHAs avoided customization to prevent future problems.
Vendor lock-in occurred commonly because HHAs lacked in-house IT resources and tried to reduce the compatibility issues
between the existing and new systems. HHAs’ service-oriented social norms and values were found to be consistent with
using HIT for improvement. The participants valued peer advice and used their association as a communication channel to
increase their HIT awareness and knowledge.
Discussion: It seems that HHAs’ clinical, administrative, and management functions require a strong coordination which
can benefit from HIT. Increasing HHAs’ awareness about existing HIE capabilities and developing better HIE infrastructures
could improve the quality of care by facilitating admissions and care delivery. Education and training of patients and care-
givers is a promising area for quality improvement. Regulatory agencies should consider that frequent changes in regulations
will require changes in HIT systems increasing HHAs’ costs.
Conclusion: The majority of the participating HHAs have made considerable progress in HIT adoption without receiving
1
University of Maryland, Baltimore County
2
Brigham and Women’s Hospital & Harvard Medical School
3
University of Maryland School of Nursing
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financial incentives. Most of them were interested in increasing their HIT adoption levels. Developing an evidence-based HIT
adoption environment and culture is likely to benefit HHAs in their HIT projects.
References
1. Resnick, HE and Alwan, M. Use of health information technology in home health and hospice agencies: United States,
2007. Journal of the American Medical Informatics Association : JAMIA 2010;17:389–395.
2. Dougherty, M, Williams, M, Millenson, M, and Harvell, J. EHR payment incentives for providers ineligible for payment
incentives and other funding study. 2013. url: aspe.hhs.gov/daltcp/reports/2013/EHRPI.pdf.
3. Everett, MR. Diffusion of innovations. 5th ed edition. Free Press, New York, 2003.
4. Ritchie, J and Spencer, L. Qualitative data analysis for applied policy research. In Alan Bryman and Robert G Burgess,
1994.
5. Ritchie, J and Lewis, J. Qualitative Research Practice: A Guide for Social Science Students and Researchers. SAGE
Publications, 2003.
6. Smith, J and Firth, J. Qualitative data analysis: the framework approach. Nurse Researcher 2011;18:52–62.
7. Gale, NK, Heath, G, Cameron, E, Rashid, S, and Redwood, S. Using the framework method for the analysis of qualitative
data in multi-disciplinary health research. BMC Medical Research Methodology 2013;13:117.
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H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
Sharing the journey: Technology-Enhanced
Transitional Palliative Care
Diane E. Holland
1
, Catherine E. Vanderboom
1
, Cory J.Ingram
1
, Ann Marie Dose
1
, Ellen Wild
2
, Kathryn H. Bowles
3
T
he majority of palliative care services are located in urban medical centers with few deliberate or consistent
approaches to coordinate care across geographically-diverse settings. This pilot study linked two proven strate-
gies, transitional care and use of health information technology, in an innovative way to extend palliative care
across settings and improve outcomes for rural patients and their caregivers. The purpose of this pilot study
was to determine feasibility, acceptability, and initial outcomes of a technology-enhanced transitional palliative care (TPC)
intervention with
Methods: In this randomized controlled trial, patients/caregivers receiving inpatient palliative care consultation in a rural
Minnesota hospital received either TPC or usual care for 8 weeks after hospital discharge. TPC consisted of one home
visit, periodic phone calls, and weekly video session visits with a nurse via iPad. Attention control patients received weekly
telephone calls by a study team member. All participants were offered a subsequent qualitative telephone interview to assess
feasibility and acceptability. Transcripts were analyzed using content analysis.
Results: Five patients and 7 caregivers were interviewed. Technology use was feasible and acceptable after minor initial
glitches were resolved; all valued viewing their nurse during video sessions. Care coordination was a dominant theme. In-
tervention patients/caregivers experienced satisfactory care coordination, enjoyed continuity provided across settings, and
valued anticipatory guidance received. Care coordination and relationship was absent for the control group; all needed to
manage care and healthcare interactions alone.
Conclusion: TPC is not only feasible, but desired by rural palliative care patients/families transitioning from hospital to
home or other care settings. Video technology was a welcomed adjunct to fostering and maintaining the provider/patient
relationship
Implications for research, policy, or practice: Palliative care should continue beyond the hospital doors; ongoing
follow-up is needed for often worsening healthcare issues for these patients. Policy needs to change to provide reimbursement
for innovative palliative care strategies that span care settings
1
Mayo Clinic Rochester
2
Mankato
3
University of Pennsylvania School of Nursing
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H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
Sustainability of Home Telepathy Programs: A
Systematic Review
Kavita Radhakrishnan
1
, Bo Xie
1
, Amy Ellis
1
A
s part of the Medicare Care Transitions Act of 2009, the federal government mandated reductions in re-
hospitalizations through better care coordination and follow up services. Remote monitoring technologies
such as telehealth has emerged as a potential solution to reduce re-hospitalization and healthcare utilization
costs and manage chronic diseases in the home health community. However, sustainability of home telehealth
programs remains a major challenge with unclear understanding of factors contributing to discontinued or sustained tele-
health use. Earlier systematic reviews have focused on the effectiveness of home telehealth programs for physiological or
behavioral outcomes,
1–4
but they have not addressed such programs’ sustainability.
Methods: To address this knowledge gap, we present here a systematic review of articles published from 1996 to 2013
within the databases of CINAHL, Pubmed/Medline, PsychInfo, Web of Science, Cochrane Reviews to identify barriers to
and facilitators for sustained telehealth use by home health patients and clinicians for chronic physiological disease man-
agement. For this review, we used the search terms of telehealth, telemonitoring, telecare, telemedicine, and telehomecare
and adapted Cradduck’s definition of sustainabilitys for telehealth services as the use of home telehealth services that holds
the promise of being absorbed into routine health-care delivery including an increasing demand for those services, as well as
acceptance of such services among healthcare providers along with a commitment to invest in them. Articles were included
if they reported on longitudinal investigations of telehealth usage by home health agencies and addressed the management
of chronic cardiovascular disease, diabetes, and obstructive pulmonary disease in older adults age 65 years or above. Data
extraction using PRISMA guidelines and quality appraisal using Mixed Methods Appraisal Tool (MMAT) was conducted
on relevant empirical studies. Thematic analysis across the studies and narrative summaries were used to synthesize the
findings from the included studies. From the final articles, the following data were extracted: (1) study design; (2) study
quality; (3) characteristics of the participants, including demographics, diagnoses, and role in the telehealth program; (4)
data collection methods; (5) description of the telehealth program model, and (6) determinants of the sustainability of home
telehealth programs.
Results: The initial 3920 citations were reduced to 943 after applying the initial search criteria and eliminating duplicates.
After title and abstract search, we abstracted 142 full articles of which 18 articles,
5–22
of moderate quality met the inclu-
sion criteria. Full-texts were retrieved by a graduate research assistant and reviewed by the first two authors. Majority
of the studies were conducted in UK (9) & US (7), with 1 in Canada and the Netherlands each. The articles are recent;
12 of the 18 studies were published after 2010. Twelve of the studies had qualitative designs; these included case study,
phenomenological, and ethnographic approaches, as well as 3 process evaluations of randomized controlled trials. The other
studies included five quantitative studies which included 1 descriptive usability study, 1 survey and 3 secondary analyses
of retrospective data; and 1 mixed methods study. Sample sizes ranged from 12 to 82 for the qualitative studies and from
132 to 403 for the survey and secondary analysis studies. Participants included only patients (10 studies), only clinicians
(4), or mixed samples of both patients and clinicians (4). Patient diagnoses targeted by the telehealth programs included
only heart failure (5 studies), only COPD (5), only diabetes (2), or any of those three chronic diseases (7). Major themes
that on sustainability of home telehealth programs included: user perceptions on effectiveness of home telehealth programs
for achieving intended outcomes, tailoring of home telehealth programs to patient characteristics and needs, communication
and collaboration among telehealth users, home health organizational processes and culture, and technology usability and
innovation.
Discussion: In summary, to realize the potential of telehealth services for chronic disease management, future program
redesign must (1) recognize formal reorganization of work between the staffs of home health service settings to include part-
nership and accountability negotiation, system interoperability, and shared visions for patient care management; (2) identify
criteria for patient haracteristics to enable telehealth service delivery tailored to individual patients’ capabilities and context;
(3) include clear guidelines and protocols for patient teaching, mechanisms for feedback and response, and negotiation of
patient responsibilities, empowering patients to become self-reliant in their care management; (4) include stakeholder input
during program implementation for improved incorporation within workflow and life routines; (5) improve technical quality
of communication; and (6) enhance device usability tailored to elder use.
Conclusion: The findings of this systematic review have important implications for sustained usage of telehealth programs
by home health service settings and can help realize the potential of telehealth for chronic disease management.
1
University of Texas
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H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
References
1. Klersy, C, De Silvestri, A, Gabutti, G, Regoli, F, and Auricchio, A. A meta-analysis of remote monitoring of heart
failure patients. Journal of the American College of Cardiology 2009;54:1683–1694.
2. Paré, G, Moqadem, K, Pineau, G, and St-Hilaire, C. Clinical effects of home telemonitoring in the context of diabetes,
asthma, heart failure and hypertension: a systematic review. Journal of Medical Internet Research 2010;12:e21.
3. Radhakrishnan, K and Jacelon, C. Impact of telehealth on patient self-management of heart failure: a review of literature.
The Journal of Cardiovascular Nursing 2012;27:33–43.
4. Udsen, FW, Hejlesen, O, and Ehlers, LH. A systematic review of the cost and cost-effectiveness of telehealth for patients
suffering from chronic obstructive pulmonary disease. Journal of Telemedicine and Telecare 2014;20:212–220.
5. Fairbrother, P, Pinnock, H, Hanley, J, McCloughan, L, Sheikh, A, Pagliari, C, McKinstry, B, and TELESCOT pro-
gramme team. Continuity, but at what cost? The impact of telemonitoring COPD on continuities of care: a qualitative
study. Primary Care Respiratory Journal: Journal of the General Practice Airways Group 2012;21:322–328.
6. Fairbrother, P, Ure, J, Hanley, J, McCloughan, L, Denvir, M, Sheikh, A, McKinstry, B, and Telescot programme team.
Telemonitoring for chronic heart failure: the views of patients and healthcare professionals - a qualitative study. Journal
of Clinical Nursing 2014;23:132–144.
7. Gale, N and Sultan, H. Telehealth as ’peace of mind’: embodiment, emotions and the home as the primary health space
for people with chronic obstructive pulmonary disorder. Health & Place 2013;21:140–147.
8. Guzman-Clark, JRS, Servellen, G van, Chang, B, Mentes, J, and Hahn, TJ. Predictors and outcomes of early adherence
to the use of a home telehealth device by older veterans with heart failure. Telemedicine Journal and E-Health: The
Official Journal of the American Telemedicine Association 2013;19:217–223.
9. Hardisty, AR, Peirce, SC, Preece, A, et al. Bridging two translation gaps: a new informatics research agenda for
telemonitoring of chronic disease. International Journal of Medical Informatics 2011;80:734–744.
10. Hibbert, D, Mair, FS, May, CR, Boland, A, O’Connor, J, Capewell, S, and Angus, RM. Health professionals’ responses
to the introduction of a home telehealth service. Journal of Telemedicine and Telecare 2004;10:226–230.
11. Horton, K. The use of telecare for people with chronic obstructive pulmonary disease: implications for management.
Journal of Nursing Management 2008;16:173–180.
12. Juretic, M, Hill, R, Hicken, B, Luptak, M, Rupper, R, and Bair, B. Predictors of attrition in older users of a home-based
monitoring and health information delivery system. Telemedicine Journal and E-Health: The Official Journal of the
American Telemedicine Association 2012;18:709–712.
13. Kaufman, DR, Pevzner, J, Hilliman, C, Weinstock, RS, Teresi, J, Shea, S, and Starren, J. Redesigning a Telehealth
Diabetes Management Program for a Digital Divide Seniors Population. Home Health Care Management & Practice
2006;18:223–234.
14. LaFramboise, LM, Woster, J, Yager, A, and Yates, BC. A technological life buoy: patient perceptions of the Health
Buddy. The Journal of Cardiovascular Nursing 2009;24:216–224.
15. Lamothe, L, Fortin, JP, Labbé, F, Gagnon, MP, and Messikh, D. Impacts of telehomecare on patients, providers,
and organizations. Telemedicine Journal and E-Health: The Official Journal of the American Telemedicine Association
2006;12:363–369.
16. Mair, FS, Hiscock, J, and Beaton, SC. Understanding factors that inhibit or promote the utilization of telecare in chronic
lung disease. Chronic Illness 2008;4:110–117.
17. Peeters, JM, Veer, AJE de, Hoek, L van der, and Francke, AL. Factors influencing the adoption of home telecare by
elderly or chronically ill people: a national survey. Journal of Clinical Nursing 2012;21:3183–3193.
18. Radhakrishnan, K, Jacelon, CS, Bigelow, C, Roche, JP, Marquard, JL, and Bowles, KH. Association of comorbidities
with home care service utilization of patients with heart failure while receiving telehealth. The Journal of Cardiovascular
Nursing 2013;28:216–227.
19. Radhakrishnan, K, Jacelon, C, and Roche, J. Perceptions on the Use of Telehealth by Homecare Nurses and Patients
With Heart Failure A Mixed Method Study. Home Health Care Management & Practice 2012;24:175–181.
20. Rogers, A, Kirk, S, Gately, C, May, CR, and Finch, T. Established users and the making of telecare work in long term
condition management: implications for health policy. Social Science & Medicine (1982) 2011;72:1077–1084.
21. Sandberg, J, Trief, PM, Izquierdo, R, et al. A qualitative study of the experiences and satisfaction of direct telemedicine
providers in diabetes case management. Telemedicine Journal and E-Health: The Official Journal of the American
Telemedicine Association 2009;15:742–750.
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H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
22. Sanders, C, Rogers, A, Bowen, R, et al. Exploring barriers to participation and adoption of telehealth and telecare
within the Whole System Demonstrator trial: a qualitative study. BMC health services research 2012;12:220.
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H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
Use and Satisfaction With Wearable Activity
Trackers Among Community Dwelling Older People
Elizabeth Madigan
1
, Chia Hua Lin
2
, Mehran Mehregany
1
T
he use of wearable devices for activity tracking (including exercise and fitness) has increased greatly yet there
is relatively little research on the use of wearable devices among older people. This is important as older users
may have different needs than younger users; their exercise and activity patterns and preferences have been
reported to be different as well. The purpose of this study was to compare three existing wearable devices on
measures of use (step and calorie counts) and satisfaction. This study is a secondary analysis of a larger study that tested
an investigator developed device and algorithms for human activity classification.
1
Methods: Thirteen community dwelling older people from a continuing care retirement community were recruited to wear
JawBone Up
TM
, Nike+ Fuelband
TM
TM and Fitbit Flex
TM
TM for one day from the time they awoke until they went to bed.
Participants wore all three devices simultaneously as part of the testing of the investigator developed device. The devices
recorded the step and calorie counts as programmed and released by the manufacturers. Falls risk assessment was done using
the Missouri Alliance for Home Care (MAHC) falls risk assessment. Following the use of the devices, the System Usability
Scale (SUS) was completed by the participants to determine satisfaction with the devices.
Results: The 13 participants ranged in age from 72-92 years; 6 were female. The falls risk assessment scores ranged from
2 to 7, with a mean = 3.84 and SD = 1.2. Based on the recommended MAHC cut score of 4 or higher for being at risk
of falls, 11 of 13 (84.6%) participants were considered at risk for falls. The recorded number of steps and calories varied
widely between the three devices. Mean step counts by device were: JawBone Up
TM
TM = 3894 (SD = 3089.8), Nike+
Fuelband
TM
TM = 2273 (SD = 1705.6) and Fitbit Flex
TM
TM = 4998 (SD = 3153). Mean calorie counts by device were:
JawBone Up
TM
TM = 2107 (SD = 152.8), Nike+ Fuelband
TM
TM = 316 (SD = 136.5) and Fitbit Flex
TM
TM = 2511 (SD =
221.2). Although the bivariate correlations between the step counts were all above 0.92, there were individual differences as
high as 6000 steps different at the individual participant level. There were low correlations between the calorie counts, with
correlations as low as 0.39 between JawBone Up and FitBit. Correlations between falls risk and step counts ranged from
-0.31 to -0.41 and between falls risk and calorie count from 0.09 to -.30; none were statistically significant although the small
sample size explains the lack of statistical significance. The SUS scores ranged from 37.5 to 82.5 on a scale from 1 to 100,
with higher scores indicating more satisfaction; the mean was 66.3 and SD was 11.9.
Discussion:The MAHC falls risk assessment has two sets of cut scores: 4 vs 6 (Calys, Gagnon and Jerrigan). Use of the
higher cut score would result in only one of the participants being considered at risk. The wide range in step counts as
measured by the device brands within each participant was surprising and may be accounted for by differences in step length,
pace of walking and different sensitivities and algorithms within each device brand. There is evidence that slower walking
speed affects the accuracy of step count from a tri-axial accelerometer (Cleland et al 2011) and that testing in non-laboratory
environments decreases the accuracy of the devices, although there was wide variation in how much the accuracy fell (Feito
2012). To determine which of the commercial devices is most accurate requires further study to measure actual step count
and step count as recorded by the devices. Performing this research with older adults with varying step lengths and walking
pace would validate the findings from other research. Calorie counts were derived from the devices based on the proprietary
algorithms. The calorie counts had even lower correlations and wider ranges within the same participants. This finding calls
in to question whether older adults want to use the calorie counts from these devices. The participants were moderately
satisfied with the devices using the SUS, perhaps from wearing all three devices simultaneously for testing the investigator-
1developed device.
Conclusion: Findings from the present study suggest that older adults are moderately satisfied with the wearable devices.
The measure of step count indicated wide variations within some participants although the correlations between the devices
were high. Further research is needed to validate the step counts and calorie tracking in community-dwelling older adults
before recommendations can be made for use of these devices by home health clinicians. While fitness devices are important
technological supports for measuring physical activity, it is not clear whether these devices will meet the needs of community-
dwelling older adults.
1
Frances Payne Bolton School of Nursing
2
Case Case Western Reserve University
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H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
References
1. Lin, CH. PhD Thesis: A Real-Time Human Posture Classifier and Fall-Detector. PhD thesis. Case Western Reserve
University, 2014.
Copyright © 2014 by Maryland Health Information Technology LLC Creative Commons License c bn d 13
H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
The Electronic Collection and Presentation of
Nocturnal Heart Failure
Cubby L. Gardner
1
, Harry B. Burke
2
H
eart failure is a major public health problem in the United States (U.S.), with high personal and institutional
costs.
1,2
Reducing heart failure readmissions is a national priority.
3,4
Most heart failure data is often have
difficulty appropriately recognizing and responding to their worsening heart failure.
5–8
Heart failure patients
experience a high burden of night time symptoms that reduce quality of life and increase their risk of hospi-
talization.
8–13
Unfortunately, there is little systematic literature addressing nocturnal heart failure, despite new findings that
a number of physiological functions exhibit circadian rhythmicity, including cardiovascular function
13
stress,
14
and extracel-
lular fluid shifts.
redolfi_s_nocturnal_2010
The current approach to the description of heart failure relies on data collected
in hospitals and clinics. The problem is that most heart failure patients experience acute exacerbations outside of hospitals
and clinics, often at night, at home, when signs and symptoms of disease are not systematically collected. This investigation
explores the application of FDA approved technology
15
to acquire information related to the detection of progressive heart
failure decompensation at home.
16
Methods: This study assesses the feasibility of using physiologic data acquisition devices and a tablet-based application to
collect disease-related data in home-dwelling heart failure patients. After data have been collected, clinicians assess the us-
ability of an electronic display of nocturnal heart failure information derived from the database of physiologic and subjective
data. This study asks the following research questions: 1) What is the feasibility of collecting ecologically valid physiological
data (heart rate, respiratory rate, blood oxygen saturation, blood pressure, and weight) and subjective data(self-assessment
features such as relative shortness of breath, swelling, pain, mood, appetite) by home-dwelling heart failure patients? 2) Can
an electronic display of physiological and psychological data be constructed that meaningfully conveys nocturnal heart failure
information? 3) What is the patients’ and clinicians’ assessment of the usability of a system for electronically collecting and
presenting nocturnal heart failure information? The setting for this study is Walter Reed National Military Medical Center.
Results: The results of the investigation are pending completion of data collection. We will determine the feasibility of
collecting and displaying physiologic and subjective data collected from home-dwelling heart failure patients. Descriptive and
summary statistics will be used to characterize the sample and describe feasibility. Patients will evaluate the us ability of the
data collection devices using the System Usability Scale, which is a 10-item instrument assessing dimensions of usability.
17
Each dimension of usability is assessed on a 5-point Likert scale. Responses are calculated to produce a score from 0 to
100, with 68 representing an average score. Intra-class correlations will be calculated across devices and patients. With the
collected data, we will create an electronic display of nocturnal heart failure information. Clinicians will assess usability of
the information display with the System Usability Scale.
17
The investigators hypothesize that the mean score will be greater
than 68. This hypothesis will be evaluated with the two-tailed Student’s t-test. The study is powered (n=37) to detect a
10-point difference at 0.80 power, alpha = 0.05.
Discussion: There is little or no research on the physiological and subjective states of home-dwelling heart failure patients
over night. In this study we investigate the feasibility and usability of a system to collect physiologic and subjective informa-
tion from heart failure patients, in their homes, at night. Then we assess clinicians’ perceptions of usability of a system to
display information constructed from the collected data. We anticipate that this electronic display will demonstrate above
average usability.
Conclusion: This study is a first step toward developing an understanding of nocturnal heart failure in home-dwelling
patients and methods to capture reliable physiologic and subjective data. We believe that this system will, in the future
provide valuable information for clinicians to improve their management of heart failure patients.
References
1. Disease Control and Prevention, C for. Centers for Disease Control and Prevention. Heart Disease Facts. 2013. url:
http://www.cdc.gov/heartdisease/facts.htm.
2. Defense, D of. Department of Defense. 2012 MHS Stakeholders Report. Military Health System. 2013. url: http :
//www.health.mil/About_MHS/StakeholdersReport.aspx.
3. ASPA. About the Law. 2013. url: http://www.hhs.gov/healthcare/rights/.
1
Daniel K. Inouye Graduate School of Nursing, Uniformed Services University of the Health Sciences
2
School of Medicine, Uniformed Services University of Health Sciences
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H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
4. Medicare, C for and Services, M. Readmissions Reduction Program. Acute Inpatient PPS - Readmissions Reduction
Program. 2014. url: http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/
Readmissions-Reduction-Program.html.
5. Friedman, MM and Quinn, JR. Heart failure patients’ time, symptoms, and actions before a hospital admission. The
Journal of Cardiovascular Nursing 2008;23:506–512.
6. Patel, H, Shafazand, M, Schaufelberger, M, and Ekman, I. Reasons for seeking acute care in chronic heart failure.
European Journal of Heart Failure 2007;9:702–708.
7. Riegel, B and Dickson, VV. A situation-specific theory of heart failure self-care. The Journal of Cardiovascular Nursing
2008;23:190–196.
8. Redeker, NS, Muench, U, Zucker, MJ, et al. Sleep Disordered Breathing, Daytime Symptoms, and Functional Perfor-
mance in Stable Heart Failure. Sleep 2010;33:551–560.
9. Andrews, LK, Coviello, J, Hurley, E, Rose, L, and Redeker, NS. "I’d eat a bucket of nails if you told me it would help
me sleep:" perceptions of insomnia and its treatment in patients with stable heart failure. Heart & Lung: The Journal
of Critical Care 2013;42:339–345.
10. Redeker, NS. Sleep disturbance in people with heart failure: implications for self-care. The Journal of Cardiovascular
Nursing 2008;23:231–238.
11. Redeker, NS and Hilkert, R. Sleep and quality of life in stable heart failure. Journal of Cardiac Failure 2005;11:700–704.
12. VS, E, CA, W, KA, D, MA, W, and A, H. Sleep disturbance symptoms in patients with heart failure. AACN clinical
issues, 2003.
13. Mheid, IA, Corrigan, F, Shirazi, F, et al. Circadian Variation in Vascular Function and Regenerative Capacity in Healthy
Humans. Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease 2014;3.
14. Wilking, M, Ndiaye, M, Mukhtar, H, and Ahmad, N. Circadian rhythm connections to oxidative stress: implications for
human health. Antioxidants & Redox Signaling 2013;19:192–208.
15. Corp, ZT. Product information web. 2014. url: http://www.zephyranywhere.com/training- systems/defense-
solutions/.
16. AL, B and GC, F. Home monitoring for heart failure management. J Am Coll Cardiol 2012;59:97–104.
17. J, B. A quick and dirty usability scale. Usability evaluation in industry. 1996.
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H3IT: Home Healthcare, Hospice, and Information Technology Conference Washington D.C., 2014
Information Practices and Information Systems in
Home Health Care: A Field-Study
Ragnhild Hellesø
1
, Merete Lyngstad
1
I
n line with the general development in health care an increasing number of people receive health care in their
homes. Caring for patients in their homes requires that providers have systems that collaborate with other
providers, with access to relevant, accurate, up-dated and situation specific information. To enhance quality
of care and efficiency in collaboration of services, the use of appropriate health information technologies (HIT)
is frequently suggested as a solution.
1
In addition different types of collaborative processes must be supported by different
technologies in order to provide proper support to the work. Most studies which have investigated health care informa-
tion practices have been conducted in hospitals.
2–5
We have not identified studies exploring home care nurses’ information
practices in-depth. The aim of this presentation is to report from an ongoing study aimed to explore home care nurses’
information practices including their collaborators in the different situations and available information.
Methods: A field study using observations, individual and group interviews were conducted. The entire material contains
totally 97 observations and 23 interviews with nurses in two Norwegian municipalities. A conceptual framework building on
a practice typology guided the overall study design. The typology describes that nursing care could be separated within four
different practice situations; acute situations, problematic situations, non-problematic situations, and problem identifying
situations. Each of the practice situations has their own distinct characteristics, though they are not mutually exclusive
categories.
6
An integrative analytical approach was used to analyze the collected data.
Results: The analysis revealed that practice situations in home health care are characterized along two different but inter-
dependent axes regarding the nurses’ information needs. Firstly, patient related axes representing a continuum from acute to
long term care situations. The second axes concerns organizational factors representing a continuum from where the nurses
collaborated with other providers in a particular situation in a limited time and space, to practice situations which required
long-time interdisciplinary and inter-organizational coordination and information. The home care nurses did not always have
access to relevant situation specific information in the different practice situations. This was partly due to lack in their HIT
system and partly due to gaps between providers in different levels of the health care system. The two different municipalities
had different HIT systems. Both systems had their advantages but also shortcomings for covering the nurses’ information
and collaboration situations.
Discussion: The findings illustrate that home care nurses need to manage different information situations. They are not
guaranteed accurate information at point of care in every situation. Their HIT systems are not developed at a level of meeting
the plurality and complexity of practice and information situations. However, the findings from the current study may be
helpful towards a more systematized development of feasible and appropriate HIT.
Conclusion: The study highlights the need for developing more appropriate and accurate HIT-systems for ensuring quality
and safe health care for patients at home.
References
1. Committee on Patient Safety and Health Information Technology, Services, BoHC, and Medicine, I of. Health IT and
Patient Safety: Building Safer Systems for Better Care. 2012. url: https://www.iom.edu:443/Reports/2011/Health-
IT-and-Patient-Safety-Building-Safer-Systems-for-Better-Care.aspx.
2. Hellesø, R, Sorensen, L, and Lorensen, M. Nurses’ information management at patients’ discharge from hospital to home
care. International Journal of Integrated Care 2005;5.
3. Webster, J, Davis, J, Holt, V, Stallan, G, New, K, and Yegdich, T. Australian nurses’ and midwives’ knowledge of
computers and their attitudes to using them in their practice. Journal of Advanced Nursing 2003;41:140–146.
4. Stevenson, JE and Nilsson, G. Nurses’ perceptions of an electronic patient record from a patient safety perspective: a
qualitative study. Journal of Advanced Nursing 2012;68:667–676.
5. Ammenwerth, E, Mansmann, U, Iller, C, and Eichstädter, R. Factors affecting and affected by user acceptance of
computer-based nursing documentation: results of a two-year study. Journal of the American Medical Informatics
Association : JAMIA 2003;10:69–84.
1
Institute of Health and Society, University of Oslo, Norway
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6. Kirkevold, M. Vitenskap for praksis? 3th. 1996. url: http : / / www . gyldendal . no / Faglitteratur / Sykepleie /
Vitenskapsteori-og-metode/Vitenskap-for-praksis.
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Notes
Notes