Dissemination of CPRs: focus on Clinical Decision Support Systems (CDSS)

Dr Emma Wallace

Dissemination of Clinical prediction rules (CPRs)

Publicly accessible register of CPRs

Clinical decision support systems

Overview of presentation

• Introduction

• Urinary tract infection (UTI) CDSS

• Implementation

Introduction

‘ Computer systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made’. 1

Key elements;- Integration in electronic patient record- Computerised format- Patient specific information

4

1. Berner E. S. Clinical Decision Support Systems in Theory and Practice. Birmingham, AL. (2 nd Ed.)

Research evidence

Clinical decision support systems

• Clinician enters clinical data of patient • Matched with system’s knowledge base• Software generates patient specific

recommendations• Provides evidence based decision support

for the clinician

Benefits

Prescribing- Improves prescribing practices- Reduces medication errors

Preventatative health- Vaccination reminders- Screening

Diagnosis- to date more limited role

CDSS: Diagnosis

• Based on Bayesian reasoning

• Pre test probability of disease estimate• Each new piece of clinical information increases or

decreases this probability (LRs)• Post test probability

Thresholds in diagnosis

Test / treatment threshold

Test / no treatment threshold

Probability of disease

100%

0%

CDSS: UTI, Diagnosis

CDSS: UTI, Risk stratification

CDSS: UTI, Management

CDSS: UTI, Prescribing

Implementation

• Success rates of CDSS increased by;

1. Automatic provision of decision support as part of clinical workflow

2. Provision of decision support at time and location of patient encounter

3. Provision of a recommendation, rather than an assessment

Barriers to implementation

1. Poor integration into clinical workflow

2. Low level of uptake by clinicians

3. Cost and time required to develop CDSS

4. GP software companies

5. Inadequate infrastructure (e.g. IT)

6. Competing demands

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