Browsing by Subject "Health Information Systems"

Now showing 1 - 3 of 3
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    Big Data Analytics for developing countries – Using the Cloud for Operational BI in Health
    (Wiley, 2013) Braa, Jørn; Purkayastha, Saptarshi
    The multi-layered view of digital divide suggests there is inequality of access to ICT, inequality of capability to exploit ICT and inequality of outcomes after exploiting ICT. This is evidently clear in the health systems of developing countries. In this paper, we look at cloud computing being able to provide computing as a utility service that might bridge this digital divide for Health Information Systems in developing countries. We highlight the role of Operational Business Intelligence (BI) tools to be able to make better decisions in health service provisioning. Through the case of DHIS2 software and its Analytics-as-a-Service (AaaS) model, we look at how tools can exploit Cloud computing capabilities to perform analytics on Big Data that is resulting from integration of health data from multiple sources. Beyond looking at purely warehousing techniques, we suggest understanding Big Data from Organizational Capabilities and expanding organizational capabilities by offloading computing as a utility to vendors through cloud computing.
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    Comparative Performance Analysis of Different Fingerprint Biometric Scanners for Patient Matching
    (IOS Press, 2017) Kasiiti, Noah; Wawira, Judy; Purkayastha, Saptarshi; Were, Martin C.; BioHealth Informatics, School of Informatics and Computing
    Unique patient identification within health services is an operational challenge in healthcare settings. Use of key identifiers, such as patient names, hospital identification numbers, national ID, and birth date are often inadequate for ensuring unique patient identification. In addition approximate string comparator algorithms, such as distance-based algorithms, have proven suboptimal for improving patient matching, especially in low-resource settings. Biometric approaches may improve unique patient identification. However, before implementing the technology in a given setting, such as health care, the right scanners should be rigorously tested to identify an optimal package for the implementation. This study aimed to investigate the effects of factors such as resolution, template size, and scan capture area on the matching performance of different fingerprint scanners for use within health care settings. Performance analysis of eight different scanners was tested using the demo application distributed as part of the Neurotech Verifinger SDK 6.0.
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    Cross-sectional comparison of critically ill pediatric patients across hospitals with various levels of pediatric care
    (Springer (Biomed Central Ltd.), 2015-11-19) Benneyworth, Brian D.; Bennett, William E.; Carroll, Aaron E.; Department of Pediatrics, IU School of Medicine
    BACKGROUND: Inpatient administrative data sources describe the care provided to hospitalized children. The Kids' Inpatient Database (KID) provides nationally representative estimates, while the Pediatric Health Information System (PHIS, a consortium of pediatric facilities) derives more detailed information from revenue codes. The objective was to contextualize a diagnosis and procedure-based definition of critical illness to a revenue-based definition; then compare it across hospitals with different levels of pediatric care. METHODS: This retrospective, cross-sectional study utilized the 2009 KID, and 2009 inpatient discharges from the PHIS database. Patients <21 years of age (excluding neonates) were included to focus on pediatric critical illness. Critical illness was defined as: (1) critical care services (CC services) using diagnosis and procedures codes and (2) intensive care unit (ICU) care using revenue codes. Demographics, invasive procedures, and categories of critical illness were compared using Chi square and survey-weighted methods. The definitions of critical illness were compared in PHIS hospitals. CC services populations identified in General Hospitals, Pediatric Facilities, and Freestanding Children's hospitals (from KID) were compared to those in PHIS hospitals. RESULTS: Among PHIS hospitals, critically ill discharges identified by CC services accounted for 37.7% of ICU care. CC services discharges were younger and had greater proportion of respiratory illness and invasive procedure use. Critically ill patients identified by CC services in PHIS hospitals were statistically similar to those in Freestanding Children's hospitals. Pediatric Facilities and General Hospitals had more adolescents with more traumas. CC services patients in general hospitals had lower use of invasive procedures and predominance of trauma, respiratory illness, mental health issues, and general infections. Freestanding children's hospitals discharged 22% of the estimated 96,700 CC services cases. Similar proportions of critically ill patients were seen in Pediatric Facilities (31%) and General Hospitals (33%). CONCLUSION: The CC services definition captured a more severely ill fraction of critically ill children. Critically ill discharges from PHIS hospitals can likely be extrapolated to Freestanding Children's hospitals and Pediatric Facilities. General Hospitals, which provide a significant amount of pediatric critical care, are different. Studies utilizing administrative data can benefit from multiple data sources, which balance the individual strengths and weaknesses.