Clinical Care Algorithms: The Good, The Bad, and The Ugly

1. Clinical Care Algorithms:The Good, The Bad, and The Ugly R. Matthew Sailors, PhD UTH Medical SchoolDepartment of Surgery

2. Overview • Modern World / Why Use Algorithms • Types of / Uses for Algorithms • Clinical Care Algorithms • Types, Use, Automation • Good, Bad, and Ugly Algorithms • Algorithm Classification & Examples • Evaluating Algorithms • Writing Good Algorithms

3. Modern World • Society is making ever greater demands on our healthcare delivery system and, in turn, on the healthcare providers. • It is imperative that the workflow of healthcare delivery be altered if quality of care and access to healthcare are to be maintained or improved. • One of the many ways of accomplishing this alteration is the automation of clinical algorithms

4. Why Do We Use Algorithms? • Share or extend expertise • Training • Disseminate processes / procedures • Reduce variability • Standardize care • Improve overall quality of service • Serve as baseline for new strategies • Medico-Legal reasons

5. Clinical Administrative Financial Time-based Data-based State-based Evidence-based Heuristics Model-based WAG Types of Algorithms

6. Clinical Care Algorithm • Specifically clinical (patient care) • NOT • Financial • Administrative • Resource allocation • Neutral, high-level term • No biases or preconceptions

7. Clinical Care Algorithm • Description of a process intended to guide sequential clinical (therapeutic or palliative) interventions. • Usually single patient-centric • Time or data-driven • Evidence-based, models, heuristic, WAG

8. Clinical Care Algorithm Care Path(way) Protocol (I) Protocol (II) Knowledge Base Practice Guideline Care Plan Procedure This is not a hierarchy diagram, just a terminology

9. Serve only as guides Only good inside the design envelope Professional clinical judgment override Handle “routine” situations Allows experts to concentrate on difficult cases Use of Clinical Algorithms

10. Automation of Clinical Algorithms • guide (but not directly provide) therapies • manage information flow • assist in diagnosis and treatment planning • provide a safety net for the patient for the times when the inevitable human / technical / system errors occur.

11. Automation of Clinical Algorithms • Computers have no native intelligence • Algorithms must be as detailed as possible • streamline the implementation process • computerized algorithm must represent • what we want to do • not just want we told the computer to do.

12. Good Algorithms -- Required • Concise description • Content and intent of the algorithm • Patient groups to which it can and cannot be safely applied • Structured, repeatable algorithm • textual or graphical form • Fully specified concepts • (e.g., “high nasogastric tube output is defined as nasogastric tube output > 1200 cc/12 hr”)

13. Good Algorithms -- Required • Fully specified decision points • E.g., PaO2 >= 60 and PaO2 <= 80 • Fully specified action steps, • Therapeutic interventions suggested by the algorithm • Calculations to be performed • Patient-specific recommendations

14. Good Algorithms -- Desired • Formal expression language • Describe the decision and action steps • Delineated scope and purpose • Define entry and exclusion criteria • Formalism to describe the flow of the algorithm from one state to the next • Encoded links • Didactics • Reference materials • On-line resources

15. Bad Algorithms • Full of vagaries (“weasel words”) • “optimize patient’s respiratory status” • Fail to adequately describe the decisions and actions that are required to care for the patient • Important entry or exclusion criteria and conditional values missing • Concepts poorly defined • “high NG output”

16. Ugly Algorithms • Unstructured / poorly structured algorithm • Algorithm follows no sequential order • Important entry or exclusion criteria appear at the end of the algorithm or in footnotes • No standard formalism used to describe algorithm

17. Algorithm Classifications • Proposal to HL7 Clinical Decision Support Technical Committee • 5 levels • 0 – 4 • Increasing detail with higher classification #

18. Class 0 • Often encoded only in textual form. • Full of vagaries • Fail to adequately describe the decisions and actions that are required to care for the patient • Actual algorithm • often unstructured or poorly structured • may follow no sequential order • Important entry or exclusion criteria and conditional values often appear at the end of the algorithm or in footnotes, if at all.

19. Class 1 • Improve upon Class 0 algorithms • All of the entry and exclusion criteria specified at the beginning of the description. • Algorithms steps are coarsely structured and are arranged in a temporal or logical progression. • Algorithms are usually still represented in textual form, but may also be represented in other forms.

20. Class 2 • Improve upon Class 1 algorithms • Explicitly defining all thresholds and decisions within the algorithms. • Some action steps are also defined.

21. Class 3 • Distinguished from Class 2 algorithms by • Representation format • Presence of definitions for all steps • Represented using structured formalism • flow diagrams • formal, structured text (pseudo-code)

22. Class 4 • Include all of the details necessary for a non-expert or computer to negotiate the algorithm in a reliable and repeatable manner. • All logical and clinical concepts are explicitly spelled out and are described in terms of patient-specific values. • Most often disseminated as either flow diagrams or encoded using a knowledge base formalism.

23. Intermediate Classifications • A given clinical algorithm may fulfill all of the requirements for a given classification and part of the requirements for a higher classification • May be necessary to classify the algorithm as a intermediate value. • Separate the two levels with a forward slash (/), such as, “Class 3 / 4”. • This notation, while less precise than a decimal or true fractional notation, has the advantage of being simple and efficient.

24. Classification Overview

25. Class 0 AED Algorithm • ABC’s, start CPR, apply AED • Push “analyze”, if indicated defibrillate at 200 J • If no conversion, defibrillate at 300 J • If no conversion, defibrillate at 360 J • Check pulse, if present, support airway • If no pulse, CPR for one minute • Check pulse, if absent press “analyze” • If advised, defibrillate up to three times at 360 J • Repeat steps 2 thru 8 until arrival at medical facility

26. Class 0 AED Algorithm (cont.) Notes: • Single rescuer with AED should verify unresponsiveness, open airway give two breaths, and check pulse. If full arrest, AED should be attached and proceed with algorithm. • Pulse checks are not required after shocks 1, 2, 4, and 5 unless “no shock indicated” is displayed • Only to be used on pulse-less, non-pediatric patients • If advanced personnel are present, they can use the manual mode • Advanced personnel can enter the above algorithm at any point and continue with appropriate advanced protocol

27. Class 1 AED Algorithm Notes: • If advanced personnel can use the manual mode • Advanced personnel can enter the algorithm at any point and continue with appropriate advanced protocol

28. Class 1 AED Algorithm (cont.) • If patient has pulse or is a pediatric patient then do not continue with algorithm. Instead use alternate algorithms for VF • Single rescuer with AED should verify unresponsiveness, open airway give two breaths, and check pulse. If full arrest, AED should be attached and proceed with algorithm. If multiple rescuers then ABC’s, start CPR, apply AED • Push “analyze”, if indicated defibrillate at 200 J • If “no shock indicated” then check pulse • If no conversion, defibrillate at 300 J • If “no shock indicated” then check pulse • If no conversion, defibrillate at 360 J • Check pulse, if present, support airway • If no pulse, CPR for one minute • Check pulse, if absent press analyze • If advised, defibrillate up to three times at 360 J • Repeat steps 3 thru 11 until arrival at medical facility

29. Class 2 AED Algorithm Notes: • If advanced personnel can use the manual mode • Advanced personnel can enter the algorithm at any point and continue with appropriate advanced protocol

30. Class 2 AED Algorithm (cont.) • If patient has pulse or patient age <= 8 years then do not continue with algorithm. Instead use alternate algorithms for VF • Single rescuer with AED should verify unresponsiveness, open airway give two breaths, and check pulse. If full arrest, AED should be attached and proceed with algorithm. If multiple rescuers then ABC’s, start CPR, apply AED • Push “analyze”, if AED displays “shock indicated”, defibrillate at 200 J • If “no shock indicated” then check pulse • If AED displays “shock indicated” (no conversion), defibrillate at 300 J • If “no shock indicated” then check pulse • If AED displays “shock indicated” (no conversion), defibrillate at 360 J • Check pulse, if present, support airway • If no pulse, CPR for one minute • Check pulse, if absent press analyze • If AED displays “shock indicated”, defibrillate up to three times at 360 J • Repeat steps 3 thru 11 until arrival at medical facility

31. Class 3 AED Algorithm

32. Class 4 AED Algorithm (Part 1)

33. Class 4 AED Algorithm (Part 2)

34. Critically Evaluating Algorithms • Identify target audience • Experts • Novices • Related fields • Identify intended use • Author’s • Yours • Look for well-defined decision and action targets (no “weasel words”) • Look for individual-based outputs

35. Critically Evaluating Algorithms • Look for well-defined decision and action targets (no “weasel words”) • Look for individual-based outputs • Use the table to help classify algorithms

36. Writing Good Algorithms • Start with general and work to specific • Iterative process • Avoid Gotchas -- later slide • Think like a child (or engineer) • Simple, discrete, decisions • Keep it simple at first • Add complexity as needed

37. Tips • Simple binary (yes / no) decisions involving 1 or 2 data points • X < 25 • X > 36 or Y <= 18 • String together lots of small steps rather than having one or two big ones • Nest complexities away

38. Gotchas • Over generalizations • “Weasel Words” • Being Too Ambitious • Not Understanding Problem Domain • Trying to Solve Wrong Problem • Trying to Use Wrong Techniques

39. Summary • Algorithms – many uses: for good, for bad • Good, bad, and ugly algorithms • Good algorithms share expertise • Algorithm classifications: 0 (low) – 4 (high) • Critically evaluate algorithms • Writing good algorithms is about attention to details