Health Information Management Journal

Hospitals are data-rich but information-poor. To develop a ‘continuous-learning health care system’ we need to harness our myriad information sources so that every patient encounter becomes the basis for new evidence of what works.

This paper reviews the documentation and coding of External causes of admitted fall cases in a major hospital. Intensive analysis of a random selection of 100 medical records included blind re-coding in the International Statistical Classification of Diseases and Related Health Problems, Tenth revision, Australian Modification (ICD-10-AM), Fifth Edition for External causes to ascertain whether: (i) the medical records contained sufficient information for assignment of specific External cause codes; and (ii) the most appropriate External cause codes were assigned per available documentation. Comparison of the hospital data with the state-wide Victorian Admitted Episodes Database (VAED) data on frequency of use of External cause codes revealed that the index hospital, a major trauma centre, treated comparatively more falls involving steps, stairs and ladders. The hospital sample reflected lower usage, than state-wide, of unspecified External cause codes and Other specified activity codes; otherwise, there was similarity in External cause coding. A comparison of researcher and hospital codes for the falls study sample revealed differences. The ambulance report was identified as the best source of External cause information; only 50% of hospital medical records contained sufficient information for specific code assignation for all three External cause codes, mechanism of injury, place of occurrence and activity at time of injury. Whilst all medical records contained mechanism of falls injury information, 16% contained insufficient details, indicating a deficiency in medical record documentation to underpin external cause coding. This was compounded by flaws in the ICD- 10-AM classification.

This paper proposes the Clinical Pathway Analysis Method (CPAM) approach that enables the extraction of valuable organisational and medical information on past clinical pathway executions from the event logs of healthcare information systems. The method deals with the complexity of real-World clinical pathways by introducing a perspective-based segmentation of the date-stamped event log. CPAM enables the clinical pathway analyst to effectively and efficiently acquire a profound insight into the clinical pathways. By comparing the specific medical conditions of patients with the factors used for characterising the different clinical pathway variants, the medical expert can identify the best therapeutic option. Process mining-based analytics enables the acquisition of valuable insights into clinical pathways, based on the complete audit traces of previous clinical pathway instances. Additionally, the methodology is suited to assess guideline compliance and analyse adverse events. Finally, the methodology provides support for eliciting tacit knowledge and providing treatment selection assistance.

Although e-health can potentially facilitate the management of scarce resources and improve the quality of healthcare services, implementation of e-health programs continues to fail or not fulfil expectations. A key contributor to the failure of e-health implementation in rural hospitals is poor quality management of projects. Based on a survey 35 participants from five rural hospitals in the Eastern Cape Province of South Africa, and using a qualitative case study research methodology, this article attempted to answer the question: does the adoption of quality assurance (QA) models add value and help to ensure success of information technology projects, especially in rural health settings? The study identified several weaknesses in the application of QA in these hospitals; however, findings also showed that the QA methods used, in spite of not being formally applied in a standardised manner, did nonetheless contribute to the success of some projects. The authors outline a generic quality assurance model (GQAM), developed to enhance the potential for successful acquisition of e-health solutions in rural hospitals, in order to improve the quality of care and service delivery in these hospitals.

Clustering in perinatal data can violate assumptions of independence, an important consideration for data analysis. Few published studies report on the extent of repeat births in routinely collected Australian perinatal data and the implications thereof for analysis and interpretation. This paper reports on a case study that examined the extent and implications of clustering in the Northern Territory Midwives Collection (NTMC) for the period 2003–2005. Data were obtained on 7,741 individual mothers giving birth to 8,707 babies in public hospitals during 2003–2005. Clusters of multiple pregnancies and repeat births were identified and the design effects for birth weight of Aboriginal and non-Aboriginal newborns were calculated. Of the mothers, 46.1% were Aboriginal. Of these, 13.2% had repeat singleton births; 0.4% had multiple pregnancies, and 0.3% had both. Of non-Aboriginal mothers, 8.7% had repeat singleton births; 1.2% had multiple pregnancies; and 0.3% had both. The design effect was 1.07 for Aboriginal newborns and 1.04 for non-Aboriginal newborns. The design effects indicate that the correct variance accounting for clustering is 4–7% larger than the incorrect variance ignoring clustering when three consecutive years of NT data are considered and an intracluster correlation coefficient of 0.48 is assumed for birth weight between twin and non-twin siblings. Depending on the outcome of interest, the impact of clustering should be considered in multivariate analysis of perinatal data, especially when such analyses involve more than one year's data, include large proportions of Aboriginal mothers and newborns, and groups with different rates of repeat births.