Studies in health technology and informatics
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Stud Health Technol Inform · Jan 2015
Developing an Emergency Physician Productivity Index Using Descriptive Health Analytics.
Emergency department (ED) crowding became a major barrier to receiving timely emergency care. At King Faisal Specialist Hospital and Research Center, Saudi Arabia, we identified variables and factors affecting crowding and performance to develop indicators to help evaluation and improvement. ⋯ Three variables were identified for their influence on productivity and performance; Number of Treated Patients per Physician, Patient Acuity Level and Treatment Time. The study suggested a formula to calculate the productivity index of each physician through dividing the Number of Treated Patients by Patient Acuity Level squared and Treatment Time to identify physicians with low productivity index and investigate causes and factors.
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Stud Health Technol Inform · Jan 2015
The Role of Medical Transcriptionists in Producing High-Quality Documentation.
This study aimed to investigate the quality-assurance work conducted by medical transcriptionists in the production of medical records, and the implications of these findings when designing a structured electronic patient record (EPR) system in which physicians are supposed to write documentation themselves. Both qualitative and quantitative methods were applied. Qualitative data were collected through informal discussions and focus-group interviews. ⋯ Each medical transcriptionist performs an average of more than six corrections per day, and approximately one of three dictations are corrected. We suggest that these correction and quality-assurance tasks need to be compensated for when designing and developing new structured EPRs. Some quality-assurance tasks may also advantageously be performed by secretaries in the future.
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Stud Health Technol Inform · Jan 2015
Comparative StudyeHealth in Saudi Arabia: Current Trends, Challenges and Recommendations.
The purpose of this study is to explore the current status of eHealth in Saudi Arabia from the perspective of health informatics professionals. We used a case study approach and analyzed participant data using thematic analysis. The study took place between July and August 2013. ⋯ The findings describe participant views on current eHealth trends in Saudi Arabia and show differences among Saudi healthcare organizations in terms of eHealth adoption. Participants also describe the challenges relating to organizational and cultural issues, end user attitudes towards eHealth projects, and the lack of specialized human resources to implement eHealth systems. Two main recommendations made by the participants were to form a new national body for eHealth and to develop a unified plan for the implementation of Saudi eHealth initiatives.
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Stud Health Technol Inform · Jan 2015
High Override Rate for Opioid Drug-allergy Interaction Alerts: Current Trends and Recommendations for Future.
This study examined trends in drug-allergy interaction (DAI) alert overrides for opioid medications - the most commonly triggered alerts in the computerized provider order entry (CPOE). We conducted an observational analysis of the DAI opioid alerts triggered over the last decade (2004-2013, n=342,338) in two large academic hospitals in Boston (United States). We found an increasing rate of DAI alert overrides culminating in 89.7% in 2013. ⋯ About one-third of the alert override reasons pointed to irrelevant alerts (i.e."Patient has tolerated the medication before") and 44.9% were unknown. Those findings warrant further investigation into providers' reasons for high override rate. User interfaces should evolve to enable less interruptive and more accurate alerts to decrease alert fatigue.
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Stud Health Technol Inform · Jan 2015
Automated Detection of Postoperative Surgical Site Infections Using Supervised Methods with Electronic Health Record Data.
The National Surgical Quality Improvement Project (NSQIP) is widely recognized as "the best in the nation" surgical quality improvement resource in the United States. In particular, it rigorously defines postoperative morbidity outcomes, including surgical adverse events occurring within 30 days of surgery. ⋯ As a prototype system, we combined local EHR data with the NSQIP gold standard outcomes and developed machine learned models to retrospectively detect Surgical Site Infections (SSI), a particular family of adverse events that NSQIP extracts. The built models have high specificity (from 0.788 to 0.988) as well as very high negative predictive values (>0.98), reliably eliminating the vast majority of patients without SSI, thereby significantly reducing the NSQIP extractors' burden.