Implementing Effective Processes to Enhance Data Analytics and Optimize Care. CHAD October 29, 2018

Transcription

1 Implementing Effective Processes to Enhance Data Analytics and Optimize Care CHAD October 29, 2018

2 Agenda Revisiting Analytics Capability Assessment Data-driven risk stratification Data Strategy Using data to determine effectiveness and ROI

3 Revisiting Analytics Capability Assessment

4 Analytics Capability Assessment: PROCESS Number of Respondents in this Range Process Analytic Capability level for most is between 5.25 and 6.75 moderately responsive to the slightly proactive. 0 Reactive Responsive Proactive Predictive

5 Analytics Capability Assessment: PROCESS Reactive Responsive Proactive Predictive Data Strategy Data strategy may be evident for specific projects and efforts such as PCMH, MU, UDS or other reporting requirements but it s not well-documented, widespread or integrated with organization strategy. Departmental plans and organizational strategy explicitly include an accompanying data strategy and analytics approach; the data strategy also addresses increasing data literacy throughout the organization. Data Governance Teams are formed to address data management for one-off initiatives when a problem or new clinical/business case requires it and depends on the project team to execute. A formal data governance project management structure is emerging in the organization to ensure that priority goals and objectives can be met and the data needed is available. Performance Measurement Performance measures developed as needed to monitor selected clinical/business processes; teams or departments are beginning to measure performance but measurement areas are not well connected. Measures are developed to monitor clinical/business process performance of strategic priorities; teams or departments measure performance in alignment with strategic goals.

6 Analytics Capability Assessment: PROCESS Reactive Responsive Proactive Predictive Data Quality Data quality reviews occur within selected teams, departments or sites but the efforts are usually one-time efforts and not sustained on an ongoing basis. Departmental data quality tracking reports are produced on a regular basis and are integrated and aligned across the organization; common errors are assessed and training occurs to address them. Analysis of Data Some teams, departments report on performance with at least quarterly frequency and produce basic dashboards and/or scorecards but they are not widely accessible or cascading. Information is available, timely and accessible to track performance on a monthly basis but varies across departments; departmental and enterprisewide data analysis cascades to all levels with some exploration using externally available data. Acting on Results Using data for improvement is recognized as important by sr. leadership but limited to major projects; some departments/sites are more successful at improvement efforts than others but there is limited accountability for measurable outcomes. Data and measurable outcomes are used routinely to demonstrate impact of prioritized improvement efforts Most dpts/sites successfully leverage data for improvement and sustainability, with some accountability for measurable outcomes.

7 Data-driven Risk Stratification

8 Roadmap to Using Risk Stratification for Population Health Management Step 1 Step 2 Step 3 Step 4 Step 5 Step 6 Step 7 c Establish the organizational culture and capability. Technology and vendor assessment Population Stratification Reengineer work processes Develop and use patient registries Pilot and rollout Measure impact and improve

9 30 Example Risk Stratification: The Process Step 1: Objective EHR Reports Other structure d data Objective Data Care Team Input Social Determina nts of Health Other external factors Step 3: Risk Assessment High Med Step 2: Subjective Low Source: Dom Dera, R. (2018, Sept.) A Practical and Team Based Approach to Risk Stratification for the Entire Patient Panel. Presentation at PCMH Congress, San Diego, CA.

10 30 Example Risk Stratification: The Details (Step 1) Use objective approaches, such as defined diagnoses, claims, or another algorithm, to risk-stratify your patients. Additions of care-team intuition occurs after this. High Risk Pre-defined risk scoring (claims, EHR) Top Quartile Utilization 3+ ED visits 1+ Readmission 1+ chron. conditions hospitalizations Admission to hospice/palliative care Clinical 1+ unstable or 2+ stable BH diagnoses 3+ active chronic conditions Active cancer Clinical measure critically out of bounds (A1C>9%) Moderate Risk Pre-defined risk scoring 2 nd and 3 rd Quartile Utilization 3+ ED visits Readmission Hospitalization related to chronic conditions Clinical 1 stable BH diagnosis Less than 3 active chronic conditions Hx of Cancer Clinical measure critically out of bounds A1C8-9% Low Risk Pre-defined risk scoring: Bottom Quartile Utilization No ED visits or hospitalization Clinical No chronic medical or behavioral health conditions Move to Step 2 of Risk Stratification Source: Dom Dera, R. (2018, Sept.) A Practical and Team Based Approach to Risk Stratification for the Entire Patient Panel. Presentation at PCMH Congress, San Diego, CA.

11 30 Example Risk Stratification: The Details (Step 2) Subjective: Concerns about SDoH Care team feels that patient is particularly at risk or living on the edge Difficulties with activities of daily living (ADLs) Referral from other provider/ care team Concerns about family supports Would you be surprised id the patient died or was institutionalized in the next year? Does the patient have complications or severe barriers to managing their chronic disease(s)? Does patient have chronic disease but with minimal long term complications; moderate social risks/ needs (as IDed through SDoH screening or through referral)? Is the patient healthy, no medical problems but with out of range biometrics? Is the patient healthy with no medical problems with normal markers? High Med Low Source: Dom Dera, R. (2018, Sept.) A Practical and Team Based Approach to Risk Stratification for the Entire Patient Panel. Presentation at PCMH Congress, San Diego, CA.

12 30 Risk Stratification and Care Optimization

13 30 Key Outcome of Risk Stratification Process 5-10% Requires case management and patient navigation capacity High 30-40% Requires team based care and coordination to address rising risk ~50% Focus on prevention and convenience Med Low

14 Data Strategy

15 Data Strategy Vision Process Ensuring that the right information is available to the right people at the right time. A data strategy is a documented plan that defines resource allocation, activities, and timeframes for addressing data acquisition, completeness, accuracy, timeliness and use, to meet organizational goals.

16 Creating a Data Strategy Data Requirements Data Governance Data Quality Granularity Integration Analysis What core data elements do you need to start with? Which ones will you need in the future? What are the sources of that data? Who owns the data element(s)? Who defines meanings and valid values? What is the division of responsibilities between admin, clinical, and IT? What validity issues are there with the required data? Availability, accuracy, consistency, timeliness? What data fixes are required? What level of detail do you need? Does the data need to be at different levels of detail for different uses? How do you get the data? Does it need to be reformatted for consistency? Does it need to feed back to other systems? What information and tools are required to perform the analysis? What skills are required to understand the data? What actions will result from the analysis?

17 Implementing a Data Strategy Data Requirements Data Governance Data Quality Granularity Integration Analysis Diagnoses, services, SDoH, demographics and where each is derived from. Documented processes and criteria, laying out when the data should be collected, by whom, for what purpose, and what acceptable responses are. Structured quality assurance process to identify gaps, inconsistencies, etc. For example, if we are identifying homeless patients, do we need details about their arrangements (i.e. are they living on the street, doubling up by staying on someone s couch, etc.)? How granular do we need race/ ethnicity data to be? Are all the data requirements available in a single system, or do they have to be combined? Does a program exist to do that? Creating risk scores requires analyzing relevant data. Defining how that should be done is key.

18 Data Requirements Diagnoses Procedures Services Demographics Social Determinants of health Already have these! PRAPARE (or other similar tools)

19 Standardized SDoH to Improve Care At an individual level both providers/ care teams and patient/ family/ caregivers empowered to improve health and wellbeing through targeted community/ social supports. At an organizational level, the health center is able to optimize care teams and services to delivered patient and communitycentered care. At a system level, integrated care is made possible by cross-sector partnerships that minimize both gaps and duplication in services, as well as increasing focus on data driven prevention and advocacy.

20 SDoH Collected with PRAPARE Race Education UDS Domains Ethnicity Veteran Status Farmworker Status English Proficiency Income Insurance Neighborhood (ZIP) Housing Status Non-UDS Domains Employment Material Security Social Isolation Stress Transportation Housing Stability

21 PRAPARE: UDS + ICD-10 Domains UDS ICD -10 Race/ Ethnicity Veteran Status Farmworker Status English Proficiency/ Preferred Language Income Insurance Neighborhood (ZIP) Housing Status (homeless, pub. housing) X X X X X X X X X Domain UDS ICD -10 Education Employment Material Security Social Isolation Stress Transportation X X X X X X

22 ICD-10 Codes for SDoH HITEQ Materials ICD-10 Z-Codes for Social Determinants of Health: Quick reference guide Coding SDoH to Optimize Value: Infographic Why Collect Standardized Data on Social Determinants of Health? Slide deck

23 Plan Responses to Based on Risk/ Need Standardized + Aligned Assessment. Assessment identifies level of risk/ severity. Level of risk/need determines response. Example: Process NACHC PRAPARE Tool High Need Moderate Need Low Need Intensive case management, wrap around services, regular follow-up. Connection to community resources, with phone follow-up or group visits. Access to services as needed. KEY Health IT EHR template ICD-10 + Enabling codes Registries/reports Referral tools

24 Using Data to Determine Effectiveness and ROI

25 30 Monitoring Data for Management Low 50% High 10% Med. 40% Whatever risk stratification approach is taken, it is important to monitor distribution of risk/ need levels across the patient population to ensure reasonable proportions identified as high, moderate, and low. Note that Care Management, Competency A in the PCMH 2017 standards is requires monitoring this.

26 30 Using Data to Design your Response High Risk 5-10%, Intensive case management, care coordination, and transition planning inc. addressing social needs, generally using RNs or Social Workers. Lots of one-on-one. Med. Risk 30-40%, Team based care, regular follow up by phone, CHW patient navigation including referral to community resources. Low Risk ~50%, Focus on prevention and convenience, alternative appointment types.

27 Beginning to Define Your ROI Identify all costs. May include hardware, software, maintenance, personnel required for the project (clinicians, data architects, outcomes analysts, knowledge managers, etc.), consulting fees, training, materials, travel, etc. Estimate benefits. Benefits must be estimated, may include using industry benchmarks for outcomes-improvement projects, vendor case studies and internal estimates. Value STEPS model can help as well. Three categories of benefits: direct benefits, indirect benefits and revenue opportunities. Identify direct benefits. Direct benefits/ savings fall into two buckets, savings from enhanced efficiency and productivity, or savings from clinical improvement and waste reduction. Efficiency/productivity savings include more effective use of internal resources; reductions in FTEs/less overtime; business process improvement; supply chain standardization; increased departmental capacity; and reductions in capital expense. Savings from clinical improvements and waste reduction include reducing unnecessary tests and procedures; lowering LOS; reductions in hospital readmissions; lower medication cost per case or per capita; and patient safety improvements leading to fewer complications or medical errors.

28 Making the Business Case with ROI Health Catalyst s Clinical Improvement Financial Tool allows you to input your assumptions to calculate returns.

29 Other Drivers of Return Often we think about ROI on PHM getting shared savings, making money on capitation contracts, and in the long term, not having to pay downside risk. Some are trying to pay for all needed investment technology, care managers, operational changes, medical homes all with the payment bucket. Two buckets that many have not put into PHM calculus: Reduction of leakage, or having patients not go elsewhere. Using IT tools, care managers, and all their different capabilities to keep people within the system can yield revenue opportunities greater than the keeping patients inside the system bucket than they are in the accountable payment bucket. Unwarranted care variation, the variability of care leading to different outcomes and increased costs. Addressing this through population health management can reduce care variation which can increase ROI as well.

30 HIMSS Value STEPS Framework Helps you take a strategic approach to analyzing and achieving value. Health centers can apply this framework by measuring improvements in:

31 Questions or thoughts?

32 Make data a part of every meeting Whether it is an executive team, clinician, finance, or all-staff meeting, have leaders, clinicians and staff use data reports together to guide discussions and decisions. example Risk-bearing provider groups report that sharing cost/ utilization data alongside quality data with clinical staff can be very helpful for identifying and acting on opportunities for quality improvement and improved financial performance.

33 Show how data can move. Ensure that no one s data role feels futile by ensuring that those tasked with various responsibility can actually DO something about the data, and that that information is actually used. example When rolling out SDoH collection, which is time consuming, staff may feel like this information is invasive or not needed. If they also don t see anything changing as a result, the effort feels wasted.

34 Use Feedback Loops Show how what portion of patients have had SDoH collected, what percentage of patients with SDoH identified have received referral to appropriate services anything that shows how the needle is moving and why this matters!

35 Interaction with data builds trust in data. Trusting the data is a longstanding challenge the more staff interact with and understand where information comes from, the more trust can be built. example Rates of uncontrolled diabetes seem stuck at 40%, higher than providers believe is accurate. The team that understands the processes and numbers behind that rate is able to overcome the skepticism.

36 Be on the same team. Data capture, monitoring, analysis, and reporting must be transparent, just as workflows and care processes must be. Transparency, leadership, and team dynamics prevent blame games and improve collaboration on the same goal.

37 Avoid initiative overload. Avoid looking at data governance or quality improvement as a project, instead focus on using data throughout all efforts. example There may be a dozen initiatives going on at any given point across the organization, then there may be departmental projects as well.

38 Consider what can be sunset. Consider beginning with a big discussion about what initiatives exist to identify what can be aligned, what can be sunset, and what needs to continue. Look at business cases and dedicated resources for each. Remember, the goal is to have data in all activities, rather than many initiatives.