Browsing by Author "Pollack, Ari H."

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    Supporting Collaborative Health Tracking in the Hospital: Patients' Perspectives
    (Association for Computing Machinery, 2018-04-21) Mishra, Sonali R.; Miller, Andrew D.; Haldar, Shefali; Khelifi, Maher; Eschler, Jordan; Elera, Rashmi G.; Pollack, Ari H.; Pratt, Wanda; Human-Centered Computing, School of Informatics and Computing
    The hospital setting creates a high-stakes environment where patients' lives depend on accurate tracking of health data. Despite recent work emphasizing the importance of patients' engagement in their own health care, less is known about how patients track their health and care in the hospital. Through interviews and design probes, we investigated hospitalized patients' tracking activity and analyzed our results using the stage-based personal informatics model. We used this model to understand how to support the tracking needs of hospitalized patients at each stage. In this paper, we discuss hospitalized patients' needs for collaboratively tracking their health with their care team. We suggest future extensions of the stage-based model to accommodate collaborative tracking situations, such as hospitals, where data is collected, analyzed, and acted on by multiple people. Our findings uncover new directions for HCI research and highlight ways to support patients in tracking their care and improving patient safety.
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    Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research
    (American Society of Nephrology, 2019-12) Denburg, Michelle R.; Razzaghi, Hanieh; Bailey, L. Charles; Soranno, Danielle E.; Pollack, Ari H.; Dharnidharka, Vikas R.; Mitsnefes, Mark M.; Smoyer, William E.; Somers, Michael J. G.; Zaritsky, Joshua J.; Flynn, Joseph T.; Claes, Donna J.; Dixon, Bradley P.; Benton, Maryjane; Mariani, Laura H.; Forrest, Christopher B.; Furth, Susan L.; Pediatrics, School of Medicine
    Background: The rarity of pediatric glomerular disease makes it difficult to identify sufficient numbers of participants for clinical trials. This leaves limited data to guide improvements in care for these patients. Methods: The authors developed and tested an electronic health record (EHR) algorithm to identify children with glomerular disease. We used EHR data from 231 patients with glomerular disorders at a single center to develop a computerized algorithm comprising diagnosis, kidney biopsy, and transplant procedure codes. The algorithm was tested using PEDSnet, a national network of eight children's hospitals with data on >6.5 million children. Patients with three or more nephrologist encounters (n=55,560) not meeting the computable phenotype definition of glomerular disease were defined as nonglomerular cases. A reviewer blinded to case status used a standardized form to review random samples of cases (n=800) and nonglomerular cases (n=798). Results: The final algorithm consisted of two or more diagnosis codes from a qualifying list or one diagnosis code and a pretransplant biopsy. Performance characteristics among the population with three or more nephrology encounters were sensitivity, 96% (95% CI, 94% to 97%); specificity, 93% (95% CI, 91% to 94%); positive predictive value (PPV), 89% (95% CI, 86% to 91%); negative predictive value, 97% (95% CI, 96% to 98%); and area under the receiver operating characteristics curve, 94% (95% CI, 93% to 95%). Requiring that the sum of nephrotic syndrome diagnosis codes exceed that of glomerulonephritis codes identified children with nephrotic syndrome or biopsy-based minimal change nephropathy, FSGS, or membranous nephropathy, with 94% sensitivity and 92% PPV. The algorithm identified 6657 children with glomerular disease across PEDSnet, ≥50% of whom were seen within 18 months. Conclusions: The authors developed an EHR-based algorithm and demonstrated that it had excellent classification accuracy across PEDSnet. This tool may enable faster identification of cohorts of pediatric patients with glomerular disease for observational or prospective studies.