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Mining Patterns Associated With Mobility Outcomes in Home Healthcare

Dey, Sanjoy; Cooner, Jacob; Delaney, Connie W.; Fakhoury, Joanna; Kumar, Vipin; Simon, Gyorgy; Steinbach, Michael; Weed, Jeremy; Westra, Bonnie L.

doi: 10.1097/NNR.0000000000000106
Feature Articles

Background: Mobility is critical for self-management. Understanding factors associated with improvement in mobility during home healthcare can help nurses tailor interventions to improve mobility outcomes and keep patients safely at home.

Objectives: The aims were to (a) identify patient and support system factors associated with mobility improvement during home care, (b) evaluate consistency of factors across groups defined by mobility status at the start of home care, and (c) identify patterns of factors associated with improvement and no improvement in mobility within each group.

Methods: Outcome and Assessment Information Set data extracted from a national convenience sample of 270,634 patient records collected from October 1, 2008 to December 31, 2009 from 581 Medicare-certified, home healthcare agencies were used. Patients were placed into groups based on mobility scores at admission. Odds ratios were used to index associations of factors with improvement at discharge. Discriminative pattern mining was used to discover patterns associated with improvement of mobility.

Results: Overall, mobility improved for 49.4% of patients; improvement occurred most frequently (80%) among patients who were able, at admission, to walk only with the supervision or assistance of another person at all times. Numerous factors associated with improvement in mobility outcome were similar across the groups (except for those who were chairfast but were able to wheel themselves independently); however, the number, strength, and direction of associations varied. In most groups, data mining-discovered patterns of factors associated with the mobility outcome were composed of combinations of functional and cognitive status and the type and amount of help required at home.

Discussion: This study provides new data mining-based information about how factors associated with improvement in mobility group together and vary by mobility at admission. These approaches have potential to provide new insights for clinicians to tailor interventions for improvement of mobility.

Sanjoy Dey, BS, is PhD Student, Department of Computer Science and Engineering, University of Minnesota, Minneapolis.

Jacob Cooner, MN, RN, is Graduate Student; and Connie W. Delaney, PhD, RN, FAAN, FACMI, is Professor and Dean, School of Nursing, University of Minnesota, Minneapolis.

Joanna Fakhoury, is Undergraduate Student; and Vipin Kumar, PhD, is William Norris Professor and Head, Department of Computer Science and Engineering, University of Minnesota, Minneapolis.

Gyorgy Simon, PhD, is Assistant Professor, Institute for Health Informatics, University of Minnesota, Minneapolis.

Michael Steinbach, PhD, is Research Associate; and Jeremy Weed, is Undergraduate Student, Department of Computer Science and Engineering, University of Minnesota, Minneapolis.

Bonnie L. Westra, PhD, RN, FAAN, FACMI, is Associate Professor, School of Nursing, University of Minnesota, Minneapolis.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s web site (www.nursingresearchonline.com).

Accepted for publication January 20, 2015.

The authors acknowledge that this study was supported by grants from the National Science Foundation (NSF IIS-1344135 and NSF IIS-0916439) and a University of Minnesota Doctoral Dissertation Fellowship.

The authors have no conflicts of interest to report.

Corresponding author: Bonnie L. Westra, PhD, RN, FAAN, FACMI, School of Nursing, University of Minnesota, 5–140 WDH, 308 Harvard St. SE, Minneapolis, MN (e-mail: westr006@umn.edu).

Accepted for publication January 19, 2015.

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