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Enhancing CDSS Alert Appropriateness in Clinical Workflow Using the Lean Method

Olufisayo O, Mohd Yusof M, Ezat Wan Puteh S

Stud Health Technol Inform, 2018;255:112-116.
PMID: 30306918

Despite the widespread use of clinical decision support systems with its alert function, there has been an increase in medical errors, adverse events as well as issues regarding patient safety, quality and efficiency. The appropriateness of CDSS must be properly evaluated by ensuring that CDSS provides clinicians with useful information at the point of care. Inefficient clinical workflow affects clinical processes; hence, it is necessary to identify processes in the healthcare system that affect provider's workflow. The Lean method was used to eliminate waste (non-value added) activities that affect the appropriate use of CDSS. Ohno's seven waste model was used to categorize waste in the context of healthcare and information technology.
A Systematic Review on CDSS Alert Appropriateness

Olakotan O, Mohd Yusof M, Ezat Wan Puteh S

Stud Health Technol Inform, 2020 Jun 16;270:906-910.
PMID: 32570513 DOI: 10.3233/SHTI200293

Clinical decision support systems (CDSSs) provides vital information for managing patients by advising clinicians through an alert or reminders about adverse events and medication errors. Clinicians receive a high number of alerts, resulting in alert override and workflow disruptions. A systematic review was carried out to identify factors affecting CDSS alert appropriateness in supporting clinical workflows using a recently introduced framework. The review findings identified several influencing factors of CDSS alert appropriateness including: technology (usability, alert presentation, workload and data entry), human (training, knowledge and skills, attitude and behavior), organization (rules and regulation, privacy and security) and process (waste, delay, tuning and optimization). The findings can be used to guide the design of CDSS alert and minimise potential safety hazards associated with CDSS use.
Applying a Constrained Optimization Portfolio Model to Aid Prioritization of Public Health Interventions in Malaysia

Varghese L, Ezat Wan Puteh S, Schecroun N, Jahis R, Van Vlaenderen I, Standaert BA

Value Health Reg Issues, 2020 May;21:172-180.
PMID: 32044690 DOI: 10.1016/j.vhri.2019.11.001

OBJECTIVES: Countries have constrained healthcare budgets and must prioritize new interventions depending on health goals and time frame. This situation is relevant in the sphere of national immunization programs, for which many different vaccines are proposed, budgets are limited, and efficient choices must be made in the order of vaccine introduction.
METHODS: A constrained optimization (CO) model for infectious diseases was developed in which different intervention types (prophylaxis and treatment) were combined for consideration in Malaysia. Local experts defined their priority public health issues: pneumococcal disease, dengue, hepatitis B and C, rotavirus, neonatal pertussis, and cholera. Epidemiological, cost, and effectiveness data were informed from local or regionally published literature. The model aimed to maximize quality-adjusted life-year (QALY) gain through the reduction of events in each of the different diseases, under budget and intervention coverage constraints. The QALY impact of the interventions was assessed over 2 periods: lifetime and 20 years. The period of investment was limited to 15 years.
RESULTS: The assessment time horizon influenced the prioritization of interventions maximizing QALY gain. The incremental health gains compared with a uninformed prioritization were large for the first 8 years and declined thereafter. Rotaviral and pneumococcal vaccines were identified as key priorities irrespective of time horizon, hepatitis B immune prophylaxis and hepatitis C treatment were priorities with the lifetime horizon, and dengue vaccination replaced these with the 20-year horizon.
CONCLUSIONS: CO modeling is a useful tool for making economically efficient decisions within public health programs for the control of infectious diseases by helping prioritize the selection of interventions to maximize health gain under annual budget constraints.

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