Determinants of frequency and longevity of hospital encounters' data use
1 Department of Biostatistics and Medical Informatics, Faculty of Medicine of University of Porto, Al Prof Hernâni Monteiro, 4200-319 Porto, Portugal
2 Centre for Research in Health Technologies and Information Systems - CINTESIS (Centro de Investigação em Tecnologias e Sistemas de Informação em Saúde), Faculty of Medicine of University of Porto, Al. Prof. Hernâni Monteiro, 4200-319 Porto, Portugal
3 Health Informatics Centre, University of Dundee, Dundee, UK
BMC Medical Informatics and Decision Making 2010, 10:15 doi:10.1186/1472-6947-10-15Published: 16 March 2010
The identification of clinically relevant information enables improvement in user interfaces and in data management. However, it is difficult to identify what information is important in daily clinical care, and what is used occasionally. This study aims to determine for how long clinical documents are used in a Hospital Information System (HIS).
The access logs of 3 years of usage of a HIS were analysed concerning report departmental source, type of hospital encounter, and inpatient encounter ICD-9-CM main diagnosis. Reports median life indicates the median time elapsed between information creation and its usage. The models that better explains report views over time were explored.
The number of report views in the study period was 656 583. Fifty two percent of the reports viewed by medical doctors in emergency encounters were from previous encounters - 21% at outpatient attendance, 19% in inpatient (wards) and 12% during emergency encounters. In an inpatient setting, 20% of the reports viewed were produced in previous encounters. The median life of information in documents is 1.5 days for emergency, 4.8 days for inpatient and 37.8 days for outpatient encounters. Immune-haemotherapy reports reach their median lives faster (7 days) than clinical pathology (15 days), gastroenterology (80 days) and pathology (118 days). The median life of reports produced in inpatient encounters varied from 36 days for neoplasms as the main diagnosis to 0.7 days for injury and poisoning. The model with the best fit (R2 > 0.9) was the exponential.
The usage of past patient information varied significantly according to patient age, type of information, type of hospital encounter and medical cause (main diagnosis) for the encounter. The exponential model is a good fit to model how the reports are seen over time, so the design of user interfaces and repository management algorithms should take it in consideration.