Going With The (Knowledge) Flow: The Future Of Decision Making In Improving Outcomes

Transcription

1 Going With The (Knowledge) Flow: The Future Of Decision Making In Improving Outcomes Ian Z. Chuang, MD, MS, CCFP SVP, Healthcare Informatics and Chief Medical Officer Dennis Morrison, PhD Chief Clinical Officer

2 CAREMANAGER TM : Coordinating care to ensure healthcare access, coordination, affordability and outcomes CARERECORD TM : A platform providing a framework for EMR, billing, scheduling, and clinical workflows CAREPATHWAYS TM : The use of data to drive optimal outcomes and operational efficiencies PLEXUS TM : On demand services, technology and delivery providing predictable results as needed, when needed CARECONNECT TM : Beyond interoperability, focused on integration, a messaging bus ensuring an open and free flowing network CAREPOINTE TM : Person-centric solutions to CAREPOINTE enable and empower TM : Person-centric solutions to the consumer, enable and empower the consumer, providing a providing a seamless integrated connection seamless integrated connection in the pursuit of in the pursuit of recovery, health and recovery, health and wellness wellness. CAREVIEW CAREPOV TM : Providing specific needs to extend capabilities, connect disparate communities and optimize user experiences

3 We are our own worst enemies Social Services MH/SU Medical Recipients of Services Clients Consumers Families People Individuals Client Consumer Patient Patient Providers of Services Direct Service Providers Social/Case Workers Clinicians Therapists Providers Doctor Nurse Provider Interventions Services Supports Treatment Therapy Treatment Therapy Intervention Documentation Service Plan Permanency Plan Individualized Service Plan Treatment Plan Service Plan Treatment Plan

4 MH/SU Family & Social Services Person/Family Medical

5 Knowledge without data is opinion.

6 Key Point about CDSS An EHR is NOT an electronic recapitulation of a paper record The EHR can and should do things for you that the paper record can t. It s not just about EHRs anymore

7 Clinician-centric CDSS 1.0

8 Why do we need CDS? It is now humanly impossible for unaided healthcare professionals to possess all the knowledge needed to deliver medical care with the efficacy and safety made possible by current scientific knowledge.

9 Definition Clinical Decision Support Systems Clinical Decision Support Systems are: active knowledge systems which use two or more items of patient data to generate case-specific advice" Wyatt J, Spiegelhalter D, 1991

10 Definition Clinical Decision Support Systems Put Another Way: If X is true and Y is true: Do THIS

11 Right info to the Right person at the Right time CDSS 2.0

12 Who gets the decision support information? CDSS 1.0 Clinicians/caregivers CDSS 2.0 Consumers Family Members Clinicians/caregivers

13 Health care and Social Services need to be transformed Less expensive professionals to do more sophisticated things in less expensive settings.

14 Digital Natives want it their way

15 The Least Expensive Provider is No Provider

16 CONSUMERS ARE ALREADY DOING DECISION SUPPORT

17 Search for online information

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22 Home Testing and Measurement

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29 Data Deluge What s the new role of care providers when consumers have more data?

30 Eric Topol, MD [the doctor s] role will be progressively morphed into providing guidance, wisdom, experience on how to transform data and information to knowledge and judgment.

31 Medicaid Self-Directed Services

32 PHRs and Portals

33 Microsoft HealthVault

34 Netsmart myhealthpointe

35 Netsmart Clinical Model Clinician Decision Support Internal Data Source (PBE) External Data Source (EBT) Pre-Intake Intake Service Plan ProgNotes Outcomes Mobile/Telemed Consumer/Family Decision Support Consumer Input Portals/PHRs Social Networking

36 Where are we today and where are we going? DECISION SUPPORT FUNCTIONALITY

37 Meaningful Use Opens the Door for CDS CDS = Clinical Decision Support Meaningful Use = a start Phasing: Stage 1 vs Stage 2 Functional capabilities focused Necessary but NOT sufficient Success is based on implementation of specific CDS interventions tied to Clinical Quality Measures (CQMs) Not looking at process and workflows

38 What is a Clinical Decision Support System? Information technology based functionality designed to improve clinical decision-making System generated information based on user data input and/or available data Presented back to the user Functionality Focused How a decision support will work within an EHR system? How will the clinician user interact with the decision support functionality? When and how will the intervention work?

39 Clinical Decision Support: The Bigger Picture Objective Focused Process Outcome Knowledge-Driven Care Process What are we trying to achieve? Desired outcomes? How can an intelligent system prompt, guide or influence the clinician user to the desired outcomes Knowledge Flow is key Don t forget about workflow

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41 Clinical Decision Support as a Process Referencing the way the nervous system works: Alerts and information display interventions are the efferent arms Based on the available data, specific message is pushed to the clinician user at the point-of-care Dashboard, reports, and benchmark are the afferent arms Data from the point-of-care come back into the central data base Analysis for knowledge-driven process improvement Both perspectives are necessary to close the loop

42 Closed-Loop Knowledge Flow

43 CARE GUIDANCE

44 Clinical Decision Support Alerts

45 CareGuidance A broad set of integrated decision support capabilities (not just alerts) that interact with the right user at the right time to have the most impact of making optimal decisions and actions At the point-of-care, the caregiver is the target user At home, and in the community, the consumer, their proxy, health coaches are the target users Care Managers and Operations Managers are target users at a system and population level

46 Clinician Workflow Opportunities for CareGuidance Targeted cohort Scheduling Hallmark events Risk identification Assessment Reference Work routing Treatment Guideline Therapy Test Medication Reference Assessment Progress Compliance Progress Performance Pre-Intake Intake Service Plan Progress Note Outcomes

47 Components of CareGuidance Knowledgebase Reference data in computer-usable form Codesets Criteria Trigger Metadata Event Date/time vs time interval Data Software algorithm Notification output Recommendation/guidance/content output User interface format

48 Information Based Decision Support Four levels of information detail Identify Inform Educate Guide Different levels of functional intervention Passive info display Workflow alerts/interruption Workflow interruption with action Interactive forms

49 Decision Support imbedded into Workflow Functionality Reference information or guidelines are actionable content with the EMR Complex set of decision support functionality structured like EMR functional components, such as Order Entry Order sets Care pathways

50 Interactive Forms/Diagnostic Tool Interactive functionality for real-time decision support Clinician provides data as requested by the tool Upon completion of data input, the tool runs an algorithm to generate an output Clinician uses the information The output may be something that is actionable and ready for user selection One advantage is the ability to run different scenarios

51 Online Reference Digital reference libraries Research paper Textbooks Info aggregators, such as Up-to-Date Functionality Info on the side Imbedded into workflow, such as ordering

52 Prognosis/Prediction Predicting health risk or events, such as risk of death Framingham APACHE Pattern matching

53 Challenges and Obstacles Information overload Lack of precision/lack of relevance Lack of time Topically correct, but wrong user audience Decision support is only as good as the data that is available/inputted Right Information, Right Person, Right Time

54 The Data Matters for CareGuidance Garbage in-garbage out Erroneous data cold as in feeling cold or a viral respiratory infection? Imprecise data diabetes, meaning diabetes mellitus or diabetes insipidus If diabetes mellitus, which one? Type I, Type II, Gestational? Data omission Is the absence of a specific data interpreted as not present or not stated? Does it matter? Rules-based CDS can be biased towards specificity (more precision), or sensitivity (more inclusion but higher rates of false positive) depending on how they are designed

55 CareGuidance Data Strategy Structure or codify the data needed for CareGuidance in pre-defined ways Support synonym Support related concepts described at different levels of granularity via concept identifiers and data dictionary anemia folate deficiency anemia, and severe iron deficiency anemia are related if we don t care about the type of anemia String match won t work so easily Same decision support rule can support different but related data Leverage standards-based codes

56 Additional Requirements for CDSS Data codified for software algorithm Data codified for monitoring and reporting Recording decision support actions for monitoring and reporting Data codified to track outcomes Dashboard and business intelligence

57 Two Sides of Knowledge-Driven Care Process If we want more evidence-based practice, we need more practice-based evidence. Evidence-Based Practice Guidance/ Suggestion do that for this reason Practice-Based Evidence did this for that reason

58 When All is Said and Done If it was your mom receiving care in the middle of a complex health system, given the option, wouldn t you prefer to have a decision support system checking on optimal care as a back-up?

59 Q&A Additional questions can be directed to: