Implementing a system for the real-time risk assessment of patients considered for intensive care.

BACKGROUND: Delay in identifying deterioration in hospitalised patients is associated with delayed admission to an intensive care unit (ICU) and poor outcomes. For the HAVEN project (HICF ref.: HICF-R9-524), we have developed a mathematical model that identifies deterioration in hospitalised patients in real time and facilitates the intervention of an ICU outreach team. This paper describes the system that has been designed to implement the model. We have used innovative technologies such as Portable Format for Analytics (PFA) and Open Services Gateway initiative (OSGi) to define the predictive statistical model and implement the system respectively for greater configurability, reliability, and availability. RESULTS: The HAVEN system has been deployed as part of a research project in the Oxford University Hospitals NHS Foundation Trust. The system has so far processed > 164,000 vital signs observations and > 68,000 laboratory results for > 12,500 patients and the algorithm generated score is being evaluated to review patients who are under consideration for transfer to ICU. No clinical decisions are being made based on output from the system. The HAVEN score has been computed using a PFA model for all these patients. The intent is that this score will be displayed on a graphical user interface for clinician review and response. CONCLUSIONS: The system uses a configurable PFA model to compute the HAVEN score which makes the system easily upgradable in terms of enhancing systems' predictive capability. Further system enhancements are planned to handle new data sources and additional management screens.

Decision-tree early warning score (DTEWS) validates the design of the National Early Warning Score (NEWS).

AIM OF STUDY: To compare the performance of a human-generated, trial and error-optimised early warning score (EWS), i.e., National Early Warning Score (NEWS), with one generated entirely algorithmically using Decision Tree (DT) analysis. MATERIALS AND METHODS: We used DT analysis to construct a decision-tree EWS (DTEWS) from a database of 198,755 vital signs observation sets collected from 35,585 consecutive, completed acute medical admissions. We evaluated the ability of DTEWS to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission or death, each within 24h of a given vital signs observation. We compared the performance of DTEWS and NEWS using the area under the receiver-operating characteristic (AUROC) curve. RESULTS: The structures of DTEWS and NEWS were very similar. The AUROC (95% CI) for DTEWS for cardiac arrest, unanticipated ICU admission, death, and any of the outcomes, all within 24h, were 0.708 (0.669-0.747), 0.862 (0.852-0.872), 0.899 (0.892-0.907), and 0.877 (0.870-0.883), respectively. Values for NEWS were 0.722 (0.685-0.759) [cardiac arrest], 0.857 (0.847-0.868) [unanticipated ICU admission}, 0.894 (0.887-0.902) [death], and 0.873 (0.866-0.879) [any outcome]. CONCLUSIONS: The decision-tree technique independently validates the composition and weightings of NEWS. The DT approach quickly provided an almost identical EWS to NEWS, although one that admittedly would benefit from fine-tuning using clinical knowledge. We believe that DT analysis could be used to quickly develop candidate models for disease-specific EWSs, which may be required in future.

STAT-HI: A socio-technical assessment tool for health informatics implementations.

This paper proposes a socio-technical assessment tool (STAT-HI) for health informatics implementations. We explore why even projects allegedly using sound methodologies repeatedly fail to give adequate attention to socio-technical issues, and we present an initial draft of a structured assessment tool for health informatics implementation that encapsulates socio-technical good practice. Further work is proposed to enrich and validate the proposed instrument. This proposal was presented for discussion at a meeting of the UK Faculty of Health Informatics in December 2009.

Developing a theoretical model of clinician information usage propensity.

Based on qualitative research, we developed the theoretical construct "clinician information usage propensity" as a hypothetical indicator of attitudes and behaviour towards clinical information and systems. We devised a survey to validate the construct and had 146 responses. Principal components analysis extracted four factors accounting for 47.2% of the variance: beliefs about clinical judgement, beliefs about information quality, cultural resistance and cognitive approach. The components were reasonably consistent with the model but two factors (beliefs about information quality, cognitive approach) had low reliability (alpha<0.6). Cultural resistance was the main factor and correlated with gender, grade and age group. Female clinicians showed significantly higher cultural resistance and preference for narrative; hospital doctors generally had higher cultural resistance than general practitioners. As only 47.2% of the variance was explained, further work is needed to refine the instrument to remove redundancy, improve sensitivity on the identified components and allow the construct to be explored as a form of technology adoption model. We posit that beliefs about clinical judgement merit further attention in medical informatics research.