Welcome! E-Health and Data Analytics: Behavioral Health

Transcription

1 Welcome! E-Health and Data Analytics: Behavioral Health

2 For the duration of this presentation, please have your cell phones available. You will be asked to use them. 2014

3 Poll Everywhere Polling application that works for live audiences using mobile devices (like phones or ipads) or Twitter.

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5 2014

6 Documentation, Classifications, Vocabularies & Data Standards Brooke Palkie, EdD

7 Documentation Providers are faced with significant changes with the transition to DSM-5 and ICD-10 Providers will have to use both the DSM and ICD criteria for diagnostic and clinical documentation purposes

8 Documentation within the EHR Additionally, the complexity of diagnosing for clinical and treatment purposes, while concurrently diagnosing for claims, will require review of workflows and data entry within the EHR. Example: Expecting a one-to-one match

9 Efficiency Back to the old adage of collecting once and use many What questions should you be asking??? What value does the EHR have with validating the diagnosis? What if you thought bigger than just tools to document.

10 Mapping DSM-5 ICD-10 Example: SNOMED CT If you want to know how many patients died with a diagnosis of heart attack last year, ICD-10 provides enough information. However, if you want more detail such as what muscle of the heart was involved, SNOMED CT is needed.

11 Example Bipolar 1 Disorder acute phase Anxious Distress partial remission full remission uspecified course features Mixed Features Rapid cycling Melancholic Features severe Atypical Features Mood-Congruent moderate mild severity Bipolar I Psychotic Features Catatonia Mood-Incongruent Peripartum Onset seasonal temporal pattern onset seasonal rapid cycling Disorder Specifiers Bipolar 1 current recent episode depressed hypomanic manic unspecified

12 Case Example Behavioral Health EHR Case Example Currently working with a behavior health organization who s EHR prompts the provider to first enter the DSM code. However, instead of the Severity and Specifiers configured as prompts for additional drop-down options, the organizations has them as optional Free Text fields. Additionally, any additional (medical diagnosis) are optional text fields. Do you see any issues here?

13 Where to start? The Golden Thread developed by Mary Thornton and her Associates Each piece of documentation must flow logically from one to another so that someone reviewing the record can see the logic Demonstration of on-going medical necessity Any element of services provided in isolation, breaks the thread Critical issues in DA, but are not in Tx plan Goals and objectives that are not individualized, but specified in the assessment Documenting clinical activities in the progress note not mentioned in the Tx Plan Without complete clinical record documentation, appropriateness of care is unclear a description of what took place in a session in relation to Tx plan the medication prescribed the client s interaction with service provider client s progress compared to the treatment plan goals future plans of treatment If it is Golden, then documentation will support each decision, intervention and diagnosis

14 Remaining Challenges Challenges remain in terms of implementation and design. What does it mean to capture data at the point of care so that it is collected in such a way that it is useful?

15 References n+the+icd-10+and+new+dsm-5%3a+many+complex+concerns

16 Discussion

17 Enabling Analytics in BH: Key Information Infrastructure Elements Piper Ranallo, PhD May 13, 2015

18 Key Point How well we capture data at the point of care dictates how useful that data For decision making at the point of care (patient-provider) For our ability to use the data for anything else

19 Important Next Steps Enhance the Information Infrastructure (Develop Informatics Tools) 1. Evidence-based clinical data sets (capture most relevant data) 2. Information models (capture valid data) 3. Standard terminologies (capture reusable data) 4. Registries and data repositories (derive actionable knowledge from the data)

20 Empirical Care v Clinical Consensus

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22 1. Evidence Based Clinical Data Sets Key Point The cost of collecting the data v. the value of the collected data is not trivial! Condition-Specific Data Sets (IOM, 2006) History and exposures Signs and symptoms Assessment and monitoring (including outcome assessment) Intervention-Specific Data Sets (IOM, 2006) Pertinent details of the intervention - IOM (2006), Marks (2009), Pincus (in process) Response to intervention (including adverse effects) Adverse Events Data Sets

23 1a. Common Data Elements

24 1b. Minimum Clinical Data Set (Literature as Source) Representation of a granular set of variables for a narrowly scoped domain such as condition, population, or intervention data elements, definitions, lists of values systematically created Functions at a level closer to patient The Need: Growing demand for EHRs in the domain of behavioral healthcare Granular data less frequently collected, and more necessary, in behavioral health care Diagnostic codes less homogenous, specific and sensitive 24

25 Our Definition of an MCDS A coherent, explicitly articulated set of standardized data elements Developed using an explicit, empirically based approach to defining and naming relevant clinical constructs Designed to optimally represent and capture data at the patient-microsystem (Berwick) interface Implemented in such a way that it can be integrated with related MDSs and/or MCDSs Oriented towards the acquisition of actionable knowledge to be used at the microsystem level 25

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27 MDS v MCDS Primary Objectives Construct Focus Data Collection Data Source Data Use Minimum Dataset (MDS) Improve Population-Level Outcomes Focus on balancing multiple stakeholder objectives Organizations Systems Intermittent, MDS-Specific Clinical Operational Organizational Regional Policy Healthcare Systems Operations Minimum Clinical Dataset (MCDS) Improve Patient-Level Outcomes Outcomes desired by individual patients Patient Microsystem During Routine Delivery of Care Patient Microsystems Patient / Community Microsystem 27

28 2. Information Models Definition A Standard way of depicting information related to some topic (example) Phase 1 Disease course, etiology, phenotypes and classification History and exposures Signs and symptoms Assessment and monitoring (including outcome assessment) Diagnosis Phase 2 Targeted interventions, mechanism of action, treatment selection Interventions Response to therapy

29 2b. Information Models Adapted from Stan Huff s adaptation of model by Linda Byrd depicting information models for Lung Cancer

30 2a. Diagnosis: DSM-5 as Source

31 2a. Example Bipolar 1 Disorder acute phase Anxious Distress partial remission full remission uspecified course features Mixed Features Rapid cycling Melancholic Features severe Atypical Features Mood-Congruent moderate mild severity Bipolar I Psychotic Features Catatonia Mood-Incongruent Peripartum Onset seasonal temporal pattern onset seasonal rapid cycling Disorder Specifiers Bipolar 1 current recent episode depressed hypomanic manic unspecified

32 3. Standard Terminologies Signs/Symptoms SNOMED-CT DSM-5 (ICD) Disorders SNOMED-CT DSM-5 (ICD) Assessments LOINC Therapies / Procedures / Interventions CPT ICD-10-PCS SNOMED-CT

33 3a. Clinical Findings SNOMED

34 3b. Assessment Instruments LOINC

35 3c. Interventions CPT Interactive complexity Psychiatric diagnostic evaluation Psychiatric diagnostic evaluation with medical services Psychotherapy with family member - 30 minutes Psychotherapy with patient - 30 minutes Psychotherapy with patient and family member - 30 minutes Psychotherapy with family member - 30 minutes with evaluation and management service Psychotherapy with patient - 30 minutes with evaluation and management service Psychotherapy with patient and family member - 30 minutes with evaluation and management service Psychotherapy with family member - 45 minutes Psychotherapy with patient - 45 minutes Psychotherapy with patient and family member - 45 minutes Psychotherapy with family member - 45 minutes with evaluation and management service Psychotherapy with patient - 45 minutes with evaluation and management service Psychotherapy with patient and family member - 45 minutes with evaluation and management service Psychotherapy with family member- 60 minutes Psychotherapy with patient - 60 minutes Psychotherapy with patient and family member - 60 minutes Psychotherapy with family member - 60 minutes with evaluation and management service Psychotherapy with patient - 60 minutes with evaluation and management service Psychotherapy with patient and family member - 60 minutes with evaluation and management service Psychotherapy for crisis - 60 minutes Psychotherapy for crisis - 30 minutes Psychoanalysis Family psychotherapy Family psychotherapy with patient present Multiple-family group psychotherapy Group psychotherapy other than of a multiple-family group Pharmacological management with psychotherapy service

36 4. Registries and Data Repositories No National Registries Some local registries (schizophrenia) CTSI Repositories In Process Low Priority Much Talk, Little Action! BPD

37 Concept Representation in the Literature Thesaurus of Psychological Index Terms PsycTESTS MeSH

38 Healthcare Information Infrastructure

39 Thank You! Questions?

40 Analytics and Big Data: Implications for Behavioral Health Ryan Sandefer

41 Time for more polling! Get out your phones!

42 So, what s all this hullabaloo about Big Data in health care?

43 Learning healthcare framework Russell, J. (2010). The role of analytics in transforming healthcare. Bio-IT World.

44 Definition: Data analytics Examination of data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and organization to make better decisions and in the sciences to verify or disprove existing models or theories.

45 Application of analytics Krohn, R. (2004). Data Analytics Throughout the Healthcare Enterprise. Journal of healthcare information management, 18(2),

46 Data analysis The task of transforming, summarizing, or modeling data to allow the user to make meaningful conclusions

47 Discussion