HIEBP- Clinical Decision Support Systems 2024 PDF

Faculty of Life Sciences & Medicine 2024 Dr Iain MBBS — Evidence Based Practice Marshall School of Life Course and and Population Sciences Clinical Decision Suppor...

Faculty of Life Sciences & Medicine 2024 Dr Iain MBBS — Evidence Based Practice Marshall School of Life Course and and Population Sciences Clinical Decision Support Systems Learning outcomes Recognise the value of Clinical Decision Support Systems (CDSS) for improving patient care Describe how a typical CDSS works Suggest situations where CDSS are likely to be helpful, and unhelpful Be aware how badly designed CDSS can hamper good patient care Clinical Decision Support Systems (CDSS) “Clinical decision support systems link health observations with health knowledge to influence health choices by clinicians for improved health care” Clinical Decision Support Systems (CDSS) Computer algorithms ‘Execute’ rules Clinician prompted by system about a way to improve care Pyramid of evidence An example problem Good blood pressure control is critical for people with diabetes It is often forgotten, not measured, or not treated adequately Paper records Electronic records plain text BP today 140/92, advised to recheck in 2 weeks, increase medication as needed Electronic records coded Blood pressure Blood pressure 142 systolic 92 diastolic 142 92 SNOMED CT An example problem (2) Obesity is an important risk factor for many conditions Lots of services exist to help people with weight loss, but there are low rates of uptake Electronic records coded Weight Weight 96 kg A CDSS solution? Can we identify people who are overweight, and offer them a referral to participate in a weight management service? Executing rules  is BMI > 30 recorded Executing rules after 31/3/2020? is ‘Weight management referral’ recorded? is ‘Weight management referral declined’ recorded? Show alert ‘Refer for weight management’ Alerts Prescribing Prescribing Prescribing Prescribing Prescribing Alert fatigue Key points Clinical decision support systems are used widely They aim to improve patient care, by integrating health data with computable knowledge The current generation of systems rely on structured health data; execute rules, and prompt clinicians to take evidence-based decisions ‘Alert fatigue’, and difficult to use systems can lead to CDSS wasting time, confusing clinicians, and potentially worsening patient care.