H3IT Logo

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.
H3IT: Home Healthcare, Hospice, and Information Technology Conference Nashville, TN, 2015
Leveraging Public Data to Investigate Home Care
Quality in Urban and Rural America
Güneş Koru
, Pooja Parameshwarappa
, Dari Alhuwail
s publicly available health-related data increase, it becomes possible to leverage such data to derive information
which can support various decisions for improving the quality of home care delivered in the United States (US).
In this respect, an important concern is to ensure that patients who live in the rural areas of the US will have
equal access to quality home care just as those in urban areas. While a number of challenges for rural home
care such as stang and transportation can be easily recognized, the urban-versus-rural variations in home care quality have
not b een suciently investigated so far.
Understanding such variations is important for leveraging health IT purposefully,
eectively, and eciently for specic quality improvement targets in urban and rural HHAs. By leveraging heterogeneous data
from disparate public repositories, this study investigated the variations in clinical pro cess, clinical outcome, and utilization
outcome measures that belong to urban and rural HHAs.
Methods: For quality measures, HHA-level data were obtained from the Medicare Home Health Compare Database (MHHC)
for 2014 which included (i) thirteen process measures, each showing the rate of adherence with a clinical practice, (ii) seven
outcome measures, each showing the rate of improvement for a clinical outcome, and (iii) two utilization outcome measures,
the rate of hospital admissions and the rate of emergency room visits not resulting in an admission. For each HHA, based on
its zipcode, a Rural Urban Area Commuting Code (RUCA) was obtained from the University of Washington (UW).
the Categorization Scheme C
suggested by UW, each HHA was categorized as eitherurban or rural. To further enrich our
results, we also obtained socio-economic status (SES) and agency characteristics data for each HHA. As a proxy for SES, we
obtained median incomes corresponding to HHA zipcodes from the Population Studies Center at the University of Michigan.
As HHA characteristics, age data was obtained from MHCC in years; patient and visit counts for 2014 were used as proxy
measures for HHA size, which were obtained from the Healthcare Cost Report Information System (HCRIS) Database.
data set excluded the private-duty HHAs or those reimbursed by local governments under Medicaid. Consequently, there
were two samples for each measure or characteristic, one for urban and other for rural HHAs. When the compared samples
had normal distributions, we used t-test for comparison; otherwise, we used the Wilcoxon-rank sum test.
In addition to
statistical signicance, we also calculated Cohen’s d for t-test and Cohen’s r for Wilcoxon test
to understand the eect
sizes, which in this case represent the magnitudes of the dierence between the means of two samples. As typically done, we
converted d values to r to use a single measure for the magnitude of dierences across dierent tests.
Smaller r values do
matter since the quality measures represent rates of care episodes.
Results: For brevity, we mention only the test results signicant at p=.01 along with the r values in parentheses. When
clinical process measures were compared, rural HHAs were better at starting care in a timely manner (.04), checking for u
shots (.03), determining whether pneumo coccal vaccine was received (.10), and checking for the risk of developing pressure
sores (.03). Urban HHAs were better at teaching patients about drugs (.07), checking for fall risks (.04), providing foot care
for diabetic patients (.04), treating pain (.05), and taking doctor-ordered action to prevent pressure sores (.01). In terms of
clinical outcomes, patients of rural HHAs had more improvement in getting in and out of bed (.06) and taking drugs correctly
by mouth (.03). Regarding the utilization outcomes, urban HHAs had better hospital admission (.16) and non-admitted ER
visit rates (.27).
Discussion: While there is plenty of room for improvement for both urban and rural HHAs, our results dispel the myth that
rural home care is worse in terms of clinical processes and outcomes. When we perform comparisons on HHA characteristics,
it seems that rural HHAs are older (.36) and they work hard; they have more patients (.09), and they make more visits (.08).
Still, rural HHAs are better in four clinical process measures and perhaps not any worse in four process measures for which
the test results did not show any statistical dierence; they are better in two clinical outcome measures and perhaps not any
worse in three others. However, regarding utilization outcomes, urban HHAs seem to be clear winners. Why is it so? It is
possible that the clinical processes at which urban HHAs perform better improve the utilization outcomes more. It is also
possible that clinical process and outcome measures do not provide a complete story: We note that median income is higher
where urban HHAs serve (.39) pointing to higher SES levels. It is a plausible conjecture that communities with lower SES
have worse health to start with, lower health literacy levels, and educational shortcomings. There may be also be geographic
isolation and limited access to community resources.
There is already some evidence that rural residents generally have
more annual hospital admissions than their urban counterparts.
Conclusion: Urban and rural HHAs have dierent strengths and weaknesses in quality of care. Therefore, health IT decisions
University of Maryland, Baltimore County
Copyright © 2015 by Maryland Health Information Technology LLC Creative Commons License c b n d 19
H3IT: Home Healthcare, Hospice, and Information Technology Conference Nashville, TN, 2015
about the selection, purchase, and customization of various solutions, can b e tailored to address dierent priorities in urban
and rural HHAs. For example, urban HHAs could focus on timely start of care while rural HHAs focus on teaching patients
about drugs via health IT adoption. Finally, it seems that the infrastructure investments made to publicize health-related
data are paying o by leading to reproducible results such as those reported in this study.
1. McCann, M, Grundy, E, and O’Reilly, D. Urban and rural dierences in risk of admission to a care home: A census-based
follow-up study. Health & place 2014;30:171–176.
2. Find and compare Home Health Agencies | Home Health Compare. url: https://www.medicare.gov/homehealthcompare/
3. UW RHRC Rural Urban Commuting Area Codes - RUCA. url: http://depts.washington.edu/uwruca/.
4. Rural Urban Commuting Area Codes Data. url: http://depts.washington.edu/uwruca/ruca-uses.php.
5. Zip Code Characteristics: Mean and Median Household Income. url: http://www.psc.isr.umich.edu/dis/census/
6. Cost Reports - Centers for Medicare & Medicaid Services. url: https://www.cms.gov/Research-Statistics-Data-
7. Nachar, N. The Mann-Whitney U: A test for assessing whether two independent samples come from the same distribu-
tion. Tutorials in Quantitative Methods for Psychology 2008;4:13–20.
8. Cohen, J. Statistical Power Analysis for the Behavioral SciencesNew JerseyLawrence Erlbaum Associates. Inc. Publishers
9. Borenstein, M, Hedges, L, Higgins, J, and Rothstein, H. Introduction to Meta-Analysis. Chapter 7. Converting Among
Eect Sizes. 2009.
10. Fritz, CO, Morris, PE, and Richler, JJ. Eect size estimates: current use, calculations, and interpretation. Journal of
experimental psychology: General 2012;141:2.
11. Boucher, MA. “Making it”: qualities needed for rural home care nursing. Home Healthcare Now 2005;23:103–108.
12. Jones, CA. Health status and health care access of farm and rural populations. 57. DIANE Publishing, 2009.
13. Reschovsky, JD and Staiti, AB. Access and quality: does rural America lag behind? Health Aairs 2005;24:1128–1139.
Copyright © 2015 by Maryland Health Information Technology LLC Creative Commons License c b n d 20