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CS5545: Medical Decision Support

CS5545: Medical Decision Support. 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. Decision Support.

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CS5545: Medical Decision Support

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  1. CS5545: Medical Decision Support

  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

  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?

  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

  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)

  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

  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

  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

  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)

  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

  11. Example

  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

  13. TSW Example

  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!”

  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) ?

  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

  17. Text Summary (human)

  18. Graphic Depiction

  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

  20. Challenges • Signal processing and data cleaning • Pattern detection, abstraction • Content selection • Microplanning and realisation • HCI

  21. Data cleaning • Get rid of artefacts • Heart rate of 0 is not possible if patient is still alive

  22. Abstraction • Pattern detection: • O2 electrode is being recalibrated • Momentary bradychardia • Property detection • Significant bradychardia • Sats are OK

  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?

  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

  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?

  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

  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…

  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

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