Co-designing a dashboard of predictive analytics and decision support to drive care quality and client outcomes in aged care: a mixed-method study protocol.

INTRODUCTION: There is a clear need for improved care quality and quality monitoring in aged care. Aged care providers collect an abundance of data, yet rarely are these data integrated and transformed in real-time into actionable information to support evidence-based care, nor are they shared with older people and informal caregivers. This protocol describes the co-design and testing of a dashboard in residential aged care facilities (nursing or care homes) and community-based aged care settings (formal care provided at home or in the community). The dashboard will comprise integrated data to provide an 'at-a-glance' overview of aged care clients, indicators to identify clients at risk of fall-related hospitalisations and poor quality of life, and evidence-based decision support to minimise these risks. Longer term plans for dashboard implementation and evaluation are also outlined. METHODS: This mixed-method study will involve (1) co-designing dashboard features with aged care staff, clients, informal caregivers and general practitioners (GPs), (2) integrating aged care data silos and developing risk models, and (3) testing dashboard prototypes with users. The dashboard features will be informed by direct observations of routine work, interviews, focus groups and co-design groups with users, and a community forum. Multivariable discrete time survival models will be used to develop risk indicators, using predictors from linked historical aged care and hospital data. Dashboard prototype testing will comprise interviews, focus groups and walk-through scenarios using a think-aloud approach with staff members, clients and informal caregivers, and a GP workshop. ETHICS AND DISSEMINATION: This study has received ethical approval from the New South Wales (NSW) Population & Health Services Research Ethics Committee and Macquarie University's Human Research Ethics Committee. The research findings will be presented to the aged care provider who will share results with staff members, clients, residents and informal caregivers. Findings will be disseminated as peer-reviewed journal articles, policy briefs and conference presentations.

Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital.

BACKGROUND: The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission. METHODS: A combination of gradient boosted tree algorithms for variable selection and logistic regression models was used to build and validate readmission risk scores using medical records from 62,235 live discharges from a metropolitan hospital in Sydney, Australia. RESULTS: The scores had good calibration and fair discriminative performance with c-statistic of 0.71 for 7-day and for 30-day readmission, and 0.74 for 60-day. Previous history of healthcare utilization, urgency of the index admission, old age, comorbidities related to cancer, psychosis, and drug-abuse, abnormal pathology results at discharge, and being unmarried and a public patient were found to be important predictors in all models. Unplanned readmissions beyond 7 days were more strongly associated with longer hospital stays and older patients with higher number of comorbidities and higher use of acute care in the past year. CONCLUSIONS: This study demonstrates similar predictors and performance to previous risk scores of 30-day unplanned readmission. Shorter-term readmissions may have different causal pathways than 30-day readmission, and may, therefore, require different screening tools and interventions. This study also re-iterates the need to include more informative data elements to ensure the appropriateness of these risk scores in clinical practice.

Measuring the effects of computer downtime on hospital pathology processes.

OBJECTIVE: To introduce and evaluate a method that uses electronic medical record (EMR) data to measure the effects of computer system downtime on clinical processes associated with pathology testing and results reporting. MATERIALS AND METHODS: A matched case-control design was used to examine the effects of five downtime events over 11-months, ranging from 5 to 300min. Four indicator tests representing different laboratory workflows were selected to measure delays and errors: potassium, haemoglobon, troponin and activated partial thromboplastin time. Tests exposed to a downtime were matched to tests during unaffected control periods by test type, time of day and day of week. Measures included clinician read time (CRT), laboratory turnaround time (LTAT), and rates of missed reads, futile searches, duplicate orders, and missing test results. RESULTS: The effects of downtime varied with the type of IT problem. When clinicians could not logon to a results reporting system for 17-min, the CRT for potassium and haemoglobon tests was five (10.3 vs. 2.0days) and six times (13.4 vs. 2.1days) longer than control (p=0.01-0.04; p=0.0001-0.003). Clinician follow-up of tests was also delayed by another downtime involving a power outage with a small effect. In contrast, laboratory processing of troponin tests was unaffected by network services and routing problems. Errors including missed reads, futile searches, duplicate orders and missing test results could not be examined because the sample size of affected tests was not sufficient for statistical testing. CONCLUSION: This study demonstrates the feasibility of using routinely collected EMR data with a matched case-control design to measure the effects of downtime on clinical processes. Even brief system downtimes may impact patient care. The methodology has potential to be applied to other clinical processes with established workflows where tasks are pre-defined such as medications management.

Real-time prediction of mortality, readmission, and length of stay using electronic health record data.

OBJECTIVE: To develop a predictive model for real-time predictions of length of stay, mortality, and readmission for hospitalized patients using electronic health records (EHRs). MATERIALS AND METHODS: A Bayesian Network model was built to estimate the probability of a hospitalized patient being "at home," in the hospital, or dead for each of the next 7 days. The network utilizes patient-specific administrative and laboratory data and is updated each time a new pathology test result becomes available. Electronic health records from 32 634 patients admitted to a Sydney metropolitan hospital via the emergency department from July 2008 through December 2011 were used. The model was tested on 2011 data and trained on the data of earlier years. RESULTS: The model achieved an average daily accuracy of 80% and area under the receiving operating characteristic curve (AUROC) of 0.82. The model's predictive ability was highest within 24 hours from prediction (AUROC = 0.83) and decreased slightly with time. Death was the most predictable outcome with a daily average accuracy of 93% and AUROC of 0.84. DISCUSSION: We developed the first non-disease-specific model that simultaneously predicts remaining days of hospitalization, death, and readmission as part of the same outcome. By providing a future daily probability for each outcome class, we enable the visualization of future patient trajectories. Among these, it is possible to identify trajectories indicating expected discharge, expected continuing hospitalization, expected death, and possible readmission. CONCLUSIONS: Bayesian Networks can model EHRs to provide real-time forecasts for patient outcomes, which provide richer information than traditional independent point predictions of length of stay, death, or readmission, and can thus better support decision making.

Cost-effectiveness analysis of a hospital electronic medication management system.

OBJECTIVE: To conduct a cost-effectiveness analysis of a hospital electronic medication management system (eMMS). METHODS: We compared costs and benefits of paper-based prescribing with a commercial eMMS (CSC MedChart) on one cardiology ward in a major 326-bed teaching hospital, assuming a 15-year time horizon and a health system perspective. The eMMS implementation and operating costs were obtained from the study site. We used data on eMMS effectiveness in reducing potential adverse drug events (ADEs), and potential ADEs intercepted, based on review of 1 202 patient charts before (n = 801) and after (n = 401) eMMS. These were combined with published estimates of actual ADEs and their costs. RESULTS: The rate of potential ADEs following eMMS fell from 0.17 per admission to 0.05; a reduction of 71%. The annualized eMMS implementation, maintenance, and operating costs for the cardiology ward were A$61 741 (US$55 296). The estimated reduction in ADEs post eMMS was approximately 80 actual ADEs per year. The reduced costs associated with these ADEs were more than sufficient to offset the costs of the eMMS. Estimated savings resulting from eMMS implementation were A$63-66 (US$56-59) per admission (A$97 740-$102 000 per annum for this ward). Sensitivity analyses demonstrated results were robust when both eMMS effectiveness and costs of actual ADEs were varied substantially. CONCLUSION: The eMMS within this setting was more effective and less expensive than paper-based prescribing. Comparison with the few previous full economic evaluations available suggests a marked improvement in the cost-effectiveness of eMMS, largely driven by increased effectiveness of contemporary eMMs in reducing medication errors.

Implementing electronic medication management at an Australian teaching hospital.

We describe the implementation of an electronic medication management system (eMMS) in an Australian teaching hospital, to inform future similar exercises. The success of eMMS implementation depends on: a positive workplace culture (leadership, teamwork and clinician ownership); acceptance of the major impact on work practices by all staff; timely system response to user feedback; training and support for clinicians; a usable system; adequate decision support.