Delay in reviewing test results prolongs hospital length of stay: a retrospective cohort study

Background: Failure in the timely follow-up of test results has been widely documented, contributing to delayed medical care. Yet, the impact of delay in reviewing test results on hospital length of stay (LOS) has not been studied. We examine the relationship between laboratory tests review time and hospital LOS. Methods: A retrospective cohort study of inpatients admitted to a metropolitan teaching hospital in Sydney, Australia, between 2011 and 2012 (n = 5804). Generalized linear models were developed to examine the relationship between hospital LOS and cumulative clinician read time (CRT), defined as the time taken by clinicians to review laboratory test results performed during an inpatient stay after they were reported in the computerized test reporting system. The models were adjusted for patients’ age, sex, and disease severity (measured by the Charlson Comorbidity index), the number of test panels performed, the number of unreviewed tests pre-discharge, and the cumulative laboratory turnaround time (LTAT) of tests performed during an inpatient stay. Results: Cumulative CRT is significantly associated with prolonged LOS, with each day of delay in reviewing test results increasing the likelihood of prolonged LOS by 13.2% (p < 0.0001). Restricting the analysis to tests with abnormal results strengthened the relationship between cumulative CRT and prolonged LOS, with each day of delay in reviewing test results increasing the likelihood of delayed discharge by 33.6% (p < 0.0001). Increasing age, disease severity and total number of tests were also significantly associated with prolonged LOS. Increasing number of unreviewed tests was negatively associated with prolonged LOS. Conclusions: Reducing unnecessary hospital LOS has become a critical health policy goal as healthcare costs escalate. Preventing delay in reviewing test results represents an important opportunity to address potentially avoidable hospital stays and unnecessary resource utilization. Electronic supplementary material The online version of this article (10.1186/s12913-018-3181-z) contains supplementary material, which is available to authorized users

Discussion of "Evidence-based health informatics:how do we know what we know?"

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Evidence-based Health Informatics: How Do We Know What We Know?" written by Elske Ammenwerth [1]. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the Ammenwerth paper. In subsequent issues the discussion can continue through letters to the editor. With these comments on the paper "Evidence-based Health Informatics: How do we know what we know?", written by Elske Ammenwerth [1], the journal seeks to stimulate a broad discussion on the challenges of evaluating information processing and information technology in health care. An international group of experts has been invited by the editor of Methods to comment on this paper. Each of the invited commentaries forms one section of this paper.11 page(s

Protocol for a randomised controlled trial examining the impact of a web-based personally controlled health management system on the uptake of influenza vaccination rates

<p>Abstract</p> <p>Background</p> <p>Online social networking and personally controlled health management systems (PCHMS) offer a new opportunity for developing innovative interventions to prevent diseases of public health concern (e.g., influenza) but there are few comparative studies about patterns of use and impact of these systems.</p> <p>Methods/Design</p> <p>A 2010 CONSORT-compliant randomised controlled trial with a two-group parallel design will assess the efficacy of a web-based PCHMS called <it>Healthy.me </it>in facilitating the uptake of influenza vaccine amongst university students and staff. Eligible participants are randomised either to obtain access to <it>Healthy.me </it>or a 6-month waitlist. Participants complete pre-study, post-study and monthly surveys about their health and utilisation of health services. A post-study clinical audit will be conducted to validate self-reports about influenza vaccination and visits to the university health service due to influenza-like illness (ILI) amongst a subset of participants. 600 participants older than 18 years with monthly access to the Internet and email will be recruited. Participants who (i) discontinue the online registration process; (ii) report obtaining an influenza vaccination in 2010 before the commencement of the study; or (iii) report being influenced by other participants to undertake influenza vaccination will be excluded from analysis. The primary outcome measure is the number of participants obtaining influenza vaccination during the study. Secondary outcome measures include: number of participants (i) experiencing ILI symptoms, (ii) absent from or experiencing impairment in work or study due to ILI symptoms, (iii) using health services or medications due to ILI symptoms; (iv) expressing positive or negative attitudes or experiences towards influenza vaccination, via their reasons of receiving (or not receiving) influenza vaccine; and (v) their patterns of usage of <it>Healthy.me </it>(e.g., frequency and timing of hits, duration of access, uptake of specific functions).</p> <p>Discussion</p> <p>This study will provide new insights about the utility of online social networking and PCHMS for public health and health promotion. It will help to assess whether a web-based PCHMS, with connectivity to a health service provider, containing information and self-management tools, can improve the uptake of preventive health services amongst university students and staff.</p> <p>Trial registration</p> <p><a href="http://www.anzctr.org.au/ACTRN12610000386033.aspx">ACTRN12610000386033</a> (Australian New Zealand Clinical Trials Registry)</p

Using cognitive models to evaluate safety-critical interfaces in healthcare

We investigated the feasibility of using a GOMSL model to assess the user interface of a medications prescribing system in a hospital setting. A GOMSL model was developed for key prescribing tasks identified in an observational study. Task execution times predicted by the GOMSL model for six clinical scenarios were comparable with times from a pilot user study. Model-based evaluation may be useful in identifying features more susceptible to errors in safety-critical healthcare interfaces.6 page(s

Quality of prescribing decision support in primary care : still a work in progress

2 page(s