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381	Exploring the understanding of routinely collected data by the health practitioners in a primary health care setting Molefi, Zachariah Modise 11 1900 (has links) Health practitioners collect health data on a daily basis at health facility levels in order to monitor and evaluate the performance of priority national health programmes (District Health Plan 2012:6). Routine data quality for health programmes monitoring need a collective intervention to ensure clear understanding for what data to be collected at primary health care setting. The aim of the study is to explore the understanding of routine health data, determine the use of routine data and feedback mechanism at primary health care clinic setting. Quantitative descriptive research design was used to answer the research question on this research study. Structured data collection questionnaire was used for the study to accomplish the research purpose and reach the study objectives. A total of 400 participants was sampled, and 247 responded. One of the findings was that the understanding of routine health data by Health Practitioners was at 82.6% (% = f/n*100, f= 3242 and n= 3926). / Health Studies Routine health data Data elements Health indicators Data quality Data validation Minimum data set Understanding of daily collected data Use of routine Information feedback presentation 362.10968227
382	Indianapolis Emergency Medical Service and the Indiana Network for Patient Care: Evaluating the Patient Match Process Park, Seong Cheol 03 January 2014 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / In 2009, Indianapolis Emergency Medical Service (I-EMS, formerly Wishard Ambulance Service) launched an electronic medical record system within their ambulances and started to exchange patient data with the Indiana Network for Patient Care (INPC). This unique system allows EMS personnel in an ambulance to get important medical information prior to the patient’s arrival to the accepting hospital from incident scene. In this retrospective cohort study, we found EMS personnel made 3,021 patient data requests (14%) of 21,215 EMS transports during a one-year period, with a “success” match rate of 46%, and a match “failure” rate of 17%. The three major factors for causing match “failure” were (1) ZIP code 55%, (2) Patient Name 22%, and (3) Birth Date 12%. This study shows that the ZIP code is not a robust identifier in the patient identification process and Non-ZIP code identifiers may be a better choice due to inaccuracies and changes of the ZIP code in a patient’s record. EMS Match Identifier INPC Ambulance Medical Record EMR Exchange Indiana Network for Patient Care Knowledge management -- Research Computer algorithms -- Research

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Exploring the understanding of routinely collected data by the health practitioners in a primary health care setting

Indianapolis Emergency Medical Service and the Indiana Network for Patient Care: Evaluating the Patient Match Process