Computational Technologies for Algorithmic Medicine

1. M Sriram Iyengar, PhD Asst. Professor, School of Health Information Sciences, University of Texas Health Science Center at Houston Informatics Research Scientist, Medical Informatics and Health Care Systems, NASA Johnson Space Center, Houston, TX John R Svirbely, MD TriHealth, Cincinnati, Ohio Computational Technologies for Algorithmic Medicine

2. Houston Overview 4th largest city in the United States, Approximate 3.5-4 M in Greater Houston Area Texas Medical Center: Largest medical center in the world contributes $11.5 billion a year to the Houston economy, 5 million patient visits per year First School of Health Information Sciences in Country

3. School of Health Information Sciences The newest UT Health Science Center school, founded in 1997 The first school in the country dedicated to graduate degrees in Health Informatics, combining the mathematics, engineering, computer, cognitive and biomedical sciences Committed to interdisciplinary, collaborative approaches to biomedical and healthcare problems Young, innovative, and dynamic faculty Students with diverse backgrounds: doctors, computer scientists, nurses, engineers, biologists, sociologists, architects, cognitive scientists, vets, public health 1 of 6 Schools in the University of Texas Health Science Center

4. Biomedical Informatics How those data are processed into health information and knowledge suitable for biomedical and healthcare tasks How computational sciences, engineering and information technology can be applied to support these processes How biological, medical, and health data are collected, stored, communicated and presented

5. Sriram Iyengar’s Biomedical Informatics Research Project #1 The Medical Algorithms Project

6. Algorithm Definitions • “A step-by-step procedure for solving a problem or accomplishing some end especially by a computer” • Any computation, formula, survey, or look-up table useful in healthcare • Amenable to numerous representations including spreadsheets, interactive web-forms

7. Motivations • Numerous algorithms (estimated at about 50,000) in health care, but most practitioners use only a small subset routinely • APGAR, BMI, Apache, Glasgow Coma scale • Algorithms would be more widely used if they were readily available in a practical format to clinicians, educators and researchers • A centralized, free repository of automated medical algorithms would be beneficial • MEDAL, www.medal.org is a web-based repository containing documentation, spreadsheets, and online web-forms. • Many potential benefits: Improve standardization, automation, reduce medical errors

8. Home page

9. More than 11,000 algorithms in 45 different medical areas

10. 400 Online web-based forms

11. Documentation page

12. Typical online web-form algorithm

13. Accomplishments so far • No. 1 in web search for ‘Medical algorithms” and ‘algorithmic medicine” • In use worldwide • Seamless (non-login) access from US Veterans Admin. Hospitals • Interest from publishers in developing companion pocket-guides • First one in Rheumatology in German from Springer • 97,204 registered users as of May 16, 2008

14. Registered users at www.medal.org

15. Future Directions • Current Limitations: • Hard to use: busy clinicians cannot type in large amounts of data • Validation: How to know which algorithms are accurate and useful? Plan: • Develop device-independent representations of medical algorithms • Integrate with Electronic Medical Records and Lab Information Systems • Develop software that matches up algorithms with patient criteria • Automatically execute queried algorithms and report results • Potential Benefits: • Enhance standardization: perhaps reduce errors • Useful decision support system • Improve documentation of care

16. Sriram Iyengar’s Biomedical Informatics Research project #2 The GuideView System

17. Low availability of physicians and highly trained nurses is a serious problem in many contexts War zones, less developed countries (Afghanistan, regions of Africa) Wherever the military provides care for civilian population CHWs and similar non-physician care providers (NPCPs) are often the initial care providers for wounded or sick personnel GuideView presents clinical guidelines in a manner that can easily be understood and used by NPCPs GuideView can potentially enhance guideline compliance leading to decreased errors and improved outcomes. When the Doctor is Really Far away!

18. Clinical Guidelines • Clinical guidelines are sequences of instructions for performing medical procedures • Can be for both diagnosis and for treatment • Typically guidelines are given as text and designed for use by physicians/nurses. Example: • Search for any evidence of an open wound in the vicinity of the fracture. • Perform a clinical examination for deformity, tenderness, or ecchymosis, or associated nerve, neurovascular, or tendon injury. Also look for the inability to perform spontaneous movement of the elbow. • Search for any evidence of dislocation and arterial vascular compromise (cold, dusky hand and forearm with loss of sensation). If found, an immediate reduction should take place (prior to x-rays if necessary). • X-ray the elbow. Special views should be obtained when necessary. (Example from www.guideline.gov, “Disorders of the elbow”) • The above is hard to understand, especially for those without formal medical training as physicians • GuideView delivers clinical guidelines in a manner that can be used and understood by persons without extensive medical training

19. GuideView Design Overview In GuideView, each step of a clinical guideline is presented in multiple modes • Text • Voice • Pictures • Full motion Video • Can be either live-motion video or animation • GuideView is interactive • Interface with GuideView using mouse clicks • Optionally use voice navigation commands

20. GuideView Technology • GuideView is Multi-Platform • Can be delivered over the web • Stand-alone Windows computers • Mobile PocketPC PDAs • Cell Phones • Screen appearance is very similar in all platforms. • GuideView can interface with medical instruments such as pulse-oximeters, wirelessly via BlueTooth • Saves time since the encoded protocol can read the medical data and traverse appropriate paths of the guideline • Reduces the need for input from humans whenever possible

21. GuideView User Interface

22. GuideView Supports Mobility • UI almost identical to that on a laptop/ desktop • Full motion video and voice output available • Weight/form factor of PDAs very desirable in many contexts including space and combat

23. BMIST (a portable EMR) - GuideView context-sensitive integration

24. BMIST - GuideView IntegrationContext Sensitive Guidelines

25. C-Guideview • GuideView for SmartPhones. • Highly portable • The guideline is stored in the cellphone memory and/or SD-card • Guidelines automatically telephones remote experts (physicians, specialists, clinic) only if algorithm determines this is needed • Enhances healthcare provider productivity and decreases costs since remote expertise is called only when really needed. • Can serve as a triage tool

26. GuideView Author • Authoring environment for creation and editing of GuideView protocols • Graphical interface for creating protocol flows • Supports insertion of text, voice, pictures, and video • Features to include automatic telephone calls, IM, email

27. GuideView Author screen

28. Future Work 2007 to 2008 • Improve integration with medical sensors • Integrate with medical algorithms (e.g. www.medicalalgorithms.com) • Add a documentation feature • Context-sensitive integration with AHLTA-Mobile • Port to new tablet hardware • Continue to improve UI of authoring component • Larger user acceptability study • Develop web-based repository of GuideViews for military purposes

29. Usability Study • Location: Human Patient Simulator Lab, WYLE Laboratories, Houston, TX • Date: March 2005 • Equipment: • Laptop computer equipped with mouse and microphone, running GuideView • Human patient simulator: a life size mannequin providing palpable pulse, respiration, chest movement • Method: Ten subjects executed two sets of guidelines (Heimlich maneuver and ILMA insertion) each according to GuideView instructions • Order of procedures and use of voice navigation for each procedure was randomized • NASA Task Load Index and a usability questionnaire were administered at the conclusion • Results analyzed by T-Tests and descriptive statistics

30. Usability Study: Results • Voice instructions had very high acceptance • 100% rated voice from useful to indispensable • In voice navigation mode, users helped ‘patient’ with both hands while listening to voiced instructions and occasionally at the video or pictures. • Video also rated highly

31. Support so far • NASA • US Army Telemedicine Advanced Technology Research Center • Microsoft Research: One of 10 winners of recent worldwide competition: “Cellphone as platform for HealthCare”. • 144 proposals were received from 25 countries

32. Sriram Iyengar’s Biomedical Informatics Research project #3 VITA: Visualization of Decision-Making in Diagnostic Testing

33. Mathematics of Diagnostic Testing • Many common tests are based on biochemical assays • PSA, BNP and others • If the assay is greater (or lesser) than a cutoff value then presence of disease is concluded • To analyze the test performance we use: • Sensitivity = P(Test positive | Disease present ) • Specificity = P(Test negative | Disease not present ) = (1 – False Positive rate) • ROC curve: Plot of Sensitivity vs. False Positive rate • Area under ROC: closer to 1 the better

34. Mathematics of Diagnostic Testing 2 • However, the Post-test Predictive Values are much more meaningful for diagnostic purposes • Positive Predictive Value = P(Disease exists| Test positive) • Negative Predicited Value = P(Disease not present | Test negative) • These can be computed using Bayes formula taking into account the disease prevalence Test negative

35. Main Vita Screen

36. Post-Test Predictive Values are complex Four Dimensional relationship!

37. VITA • Interactive software for 3-D visualization • 3-D and 4-D views • Rotation, zooming and similar functions for graph manipulation • Can use a table of cut-off values and generate direct relationships between PPV, NPV and cutoff values for various prevalences

38. 4-D Plot

39. Pred. Values vs cutoff values

40. VITA Benefits • Teaching: • Important and complex mathematical concepts in medical decision-making • Understand non-linearity in predictive values • Research: • Determine optimal cutoff-vales for diagnostic tests • Compare performance of diagnostic tests on the basis of predictive vales that are more meaningful to diagnosticians

41. Thank You!!