CS5545: Medical Decision Support

Slide 1:CS5545: Medical Decision Support

Slide 2:Medical DIC

Data Interpretation and Communication is important in many ways in medicine Exploring data, especially in research HCE and genomic data (next week) Helping doctors, nurses, and patients make decisions Informing patients

Slide 3:Decision Support

Examples Doctor: What’s wrong with a patient, and should he/she be treated? Nurse: Should I do X (pick up baby, call doctor, etc)? Patient: Do the benefits of a procedure outweigh the costs?

Slide 4:Decision Support

Doctors don’t like being told what to do So MYCIN-like medical expert systems not very effective Doctors do like tools which present the relevant information in a nice way Data interpretation and communication

Slide 5:Medical Data (Patient Record)

Background: birthdate, sex, family history, … Does not (usually) change over time Observations Qualitative (eg, symptoms) Quantitative (eg, lab results, sensors, images) Diagnoses (interpretations) Actions Treatments (medication, surgery, etc) Other (visits by friends, doctor chats, etc)

Slide 6:Medical Data

Usually time-indexed (except for background info) Observations, diagnoses, actions Likely to be distributed over several DB We’ll ignore, but huge problem in practice Technical integration issues Privacy issues

Slide 7:Visualising Medical Data

How can we visualise such a diverse time-varying data set? Overview and details-on-request Initially show user an overview User clicks for more details Horizontal time-axis Icons, graphs, textual annotations, etc depending on type of data

Slide 8:Example 1: Lifelines

Lifelines (developed by HCIL, Maryland) visualises a patients medical record http://www.cs.umd.edu/hcil/lifelines/ Can also be used for e-commerce Timeline showing problems, diagnoses, lab results, etc Unclear if ever used in practice

Slide 9:Lifelines Overview

Fits on one screen No scrolling (important to HCIL) Focuses on qualitative data Textual annotations, not icons User clicks for details Including quantitative data (lab results, scans, etc)

Slide 10:Ex 2: Badger

Commercial system to display “bedside” data to a doctor or nurse http://www.clevermed.com Focuses on quantitative data Heart rate, Oxygen, etc Also shows notes Same screen, or special screen Scroll/zoom interface

Slide 11:Example

Slide 12:Ex 3: Time Series Workbench

TSW (developed by Prof Hunter, Aberdeen) visualises ICU data In some ways a more sophisticated version of Badger, used in many research projects Notes and other qualitative events are time-indexed and colour coded Customised for different projects

Slide 13:TSW Example

Slide 14:Medical Visualisation

Unclear how much visualisation systems actually help doctors make decisions Definitely useful for research! Some studies have shown that Badger/TSW like systems do not improve clinical outcomes Although doctors like having them? Too much data for a time-pressured doctor to absorb??? “When the alarm rings at 3AM, I don’t want 20 pages of graphs!”

Slide 15:Less Information?

Current systems focus on displaying raw data to doctors Should focus more on summarising, interpreting, abstracting data for doctors? Maybe use text (NLG) ?

Slide 16:BabyTalk

BabyTalk project: show doctors (nurses) textual summaries of medical data Short summaries, which are highly summarised and abstracted Research project, started Sept 06 Initial experiment suggests doctors make better treatment decisions when shown human-written text summaries

Slide 17:Text Summary (human)

Slide 18:Graphic Depiction

Slide 19:BabyTalk Systems

BT-Doc: computer texts help doctors make decisions BT-Nurse: computer texts help nurses write reports BT-Parent: computer texts explain what is happening to parents

Slide 20:Challenges

Signal processing and data cleaning Pattern detection, abstraction Content selection Microplanning and realisation HCI

Slide 21:Data cleaning

Get rid of artefacts Heart rate of 0 is not possible if patient is still alive

Slide 22:Abstraction

Pattern detection: O2 electrode is being recalibrated Momentary bradychardia Property detection Significant bradychardia Sats are OK

Slide 23:Content Selection

Focus on spikes (bradycardia) in HR instead of on level? Only level comment is falling at end Highlight changes in FiO2 settings Oxygen level in incubator Based on what is important Implicitly guides diagnosis? Similar reasoning to medical expert syst?

Slide 24:Other NLG issues

Mention events in time order Don’t describe signals independently Integrate events (eg,morphine given), settings (eg, FiO2), sensors (BP) into a “story” Rhetorical structure … but the sats have remained OK Lexical choice Bradycardia vs downward spike in HR Aggregation

Slide 25:HCI Issues

How combine NLG and visualisation? Text gives overview, hyperlinks to detailed graphical displays??? How do users interact with system? Different presentations for doctors, nurses, parents?

Slide 26:Evaluation

How do we test if BabyTalk works? What is impact of BabyTalk? Better decision making – measure decision correctness in “off-ward” experiment Faster report-writing – measure time ro post-edit BT reports vs write from scratch Lower stress (parents) – use psych test to measure stress levels in parents

Slide 27:Path to Real World

4 years to develop technology (current BabyTalk project) 4 years to develop product Commercial-quality software (robust, well documented, portable, etc) Clinical trial of effectiveness 4 years to get widely used in NHS Probably optimistic…

Slide 28:Medical decision support

Goal: help doctors and nurses (and patients) decide on diagnoses and treatments Current practice: information-rich visualisations Future (?): more focused info presentations, possibly text as well as visualisations