Where to put the Intelligent Systems in healthcare?

1. Where to put the Intelligent Systems in healthcare? Jim WarrenProfessor of Health Informatics http://www.cs.auckland.an.nz/~jim/

2. Potential roles • Support at the point of care • Classic ‘decision support system’ (we prefer not to say ‘expert system’!) • Support for the health consumer • As such they may or may not be a ‘patient’ • Support for ‘Population Health’ • Identify big trends, clusters and at-risk groups

3. Why expert systems are hard • Any decision algorithms will have an error rate • Sum of Type I and Type II errors, each with different consequences • And if you’re too cautious, you’ll cause ‘alert fatigue’

4. Focus on adherence • ‘Adherence’ is the extent to which a patient undertakes a therapeutic regimen in the manner directed by their healthcare provider • Sometimes called ‘compliance’ • Ideally it emerges from good communication and ‘concordance’

5. Let’s add a robot! That’ll help, right? • Medications process at a typical elder-focused community: Lots of players • Doctors (GP, specialist) prescribe • Pharmacists dispense • Residents take their medication as directed • Some live as couples • May be helped by nurse • Supported by family and caregivers

6. So what about the elder-robot situation? • High need for adherence promotion • Elderly usually have complex medication regimens (often over 5 long-term medications) • High risk • Medication misadventure is a frequent cause of hospitalisations in the elderly • But patient empowerment / autonomy are critical issues • Elderly are already struggling on these issues and now we’re introducing a large anthropomorphic machine into their lives

7. “Charlie” – started life as a vacuum cleaner, then became ‘Cafero’

8. Patient data Based on extracts from the prescribing software uploaded to a robot server (Robogen) with stored profiles of patient details and preferences

9. Must avoid ‘intimidation’ Take your medication now or you will be EXTERMINATED!!!

10. Must avoid robot-as-’snitch’ Take that pillNOW or I’m telling all the doctors and nurses that you’re ignoring their advice

11. Solution 1: Give options • Create an ‘out’ for taking the medication now • Allow defer (like a snooze alarm) • Accept that patient doesn’t want to take this dose • Make turning down the offer a ‘normal’ path • Make informing healthcare providers of a missed dose an option • Can sound a lot more positive that way

12. Solution 2: seek understanding • Don’t pretend to know everything • Lots of reasons the robot could be wrong when prompting to take a medication • Offer a pick list of a few common reasons for non-adherence

13. Balancing adherence and safety

14. Solution 3: provide more help • Offer information about the medications • Currently text, but can expand to video • Check about side-effects • Most medications have a couple common side-effects that account for most of the trouble • We throw in periodic checks on these, alternating between general and situated questions • Also provide safety through physiological monitoring • Indeed I don’t think we would’ve gotten research ethics approval to leave people alone with the robot without this feature!

15. Customised questions to probe undesirable symptoms One of these questions would randomly appear at the end of each session

16. Weaving the social network • Our goal is to empower • Provide the prescribing physician with more detail about the adherence challenges facing their elderly patients • Step the patient through taking their medications in a knowledgeable fashion • We believe it’s a healthy alternative to taking them out of the loop with a dispensing machine • Has met with good acceptance in small trials so far

17. In the end we went small iRobiQ from Yujin Robotics South Korea Many features: e.g. 8 is a floor detection sensor9 is a bumper sensor iRobiQ taking a blood pressure

18. Quality Issues in discharge summaries (Makes them hard to use for everybody, but especially for patients and their families!) • Irrelevant information • Missing results/ interpretation • Information hard to access • Information written in other section • Poor formatting • Incomplete follow up advice • No useful information • Missing important information • Information is written in other section • Insufficient information • Unclear goals • Incorrect, incomplete and missing advice • Information written in other section • No useful synopsis • Brief or incomplete advice • Missing information • Use of abbreviation and medical jargon

19. IT-based Remediation Plan SemAssist – intelligent agent on top of summary authoring environment Interactive Decision Support Recommend Advice to Patient Writing Support Critique Advice to Patient Patient Support Semantic Annotation Synonym Provision Reading Support Hyperlink to Explanatory Material SemLink – based on near-future where patient reads discharge summary online

20. Writing Support(Ontology Model) Patient Age, Sex hasHighRiskDischargeMedication High Risk Discharge Medication Anticoagulant Cardiovascular Anti-Infective Analgesic Corticosteroid hasPatientInformation • Warfarin • Marevan • Warfarin • Warfarin Sodium ACE Inhibitor Amoxicillin Acetaminophen Prednisone Medication Category Layer Digoxin Quinine Morphine Ibuprofen GlycerylTrinitrate Patient Information Medication Information Layer Side Effect Patient Action Follow up • Warfarin Patient Action • Prescribed Dose Adherence • Avoid Alcohol • Avoid Salicylates • Avoid Green Leafy Vegetables • Avoid Cranberry Juice • Avoid Vitamin K Dairy Products • Dietary Change • Start Medication • Stop Medication • Warfarin Follow up • Blood Test • INR • Warfarin Side Effect • Abnormal Urine • Severe Headache • Loose Stool • Hemoptysis • Easy Bruising • Gum Nose Bleeding hasPatientAction hasFollowup hasSideEffect

21. Consumer health search engines used in Hyperlinking for reading support

22. SemLink in Portable Document Format (PDF)

23. Skin and Subcutaneous Tissue Infection (SSTI) • Common cause of avoidable hospitalisation in New Zealand • Often caused by staph and strept bacteriological infections • Treatable with antibiotics but delay or non-adherence can cause complications leading to hospitalisation • Can we detect households with recurrent SSTIs in children based on the electronic medical record (EMR) in General Practice?

24. Ontology model

25. SSTI identification flowchart • 36% of individuals age 20 or under had recurrent SSTI from 4 Auckland practices serving substantial Māori and Pacific population • 65% of cases identified by notes (not diagnosis code or lab test) • Hence, needed NLP

26. Conclusion • Exciting applications of AI technology to improve healthcare delivery • The AI is not always delivered as an explicit ‘agent’ • May be hidden as a subtle part of the user interface or in a detection algorithm • Range of users and settings • Point of care, health consumer, population health