Analytics and Insights: Data Driven Clinical Quality

Presented by Mary McKendry at the Executive Leadership Summit on March 24 - 26, 2015.

The Institute for Healthcare Improvement (IHI) takes a unique approach to improving quality, safety, and value in health care. This approach is called the science of improvement. According to IHI, the science of improvement is an applied science that emphasizes innovation, rapid-cycle testing in the field, and spread/controlled expansion in order to generate learning about what changes, in which contexts, produce improvements.

Supporting innovative and successful approaches for clinical quality improvement requires data, transformed into useful information. Understanding what data sources to use and how to use them is imperative. By utilizing quality process improvement models such as The Donabedian Model, a conceptual model that provides a framework for examining health services and evaluating quality of care, health care organizations should be able to use their data to improve their clinical quality performance.

Transcript

1. Mary Jane McKendry Vice President Quality Management Neighborhood Health Plan
2. Analytics and Insights: Data Driven Clinical Quality 2
3. Key Takeaways This discussion will help participants: • Think of innovative ways to use organizational data to drive clinical quality improvement • Consider the use of health care quality process improvement models to develop their strategies 3
4. Prioritize Clinical Quality Improvement • Identify goals • Understand data structure • Identify data sources • Select improvement methodology / framework • Develop plan • Implement • Validate, course correct, if needed 4
5. Identify Goals Ask and answer the following: • What are the drivers for clinical quality goals? • Does data / information exist to support the goals? • Are the goals achievable with: – Timeframes anticipated? – Existing resources? – Existing programs, deliverables, priorities? • What may have to change to achieve the goals? – Structure – Processes – Available data 5
6. Understand Data Structure Eliminate the guess work and use quality data to drive improvement decisions: • Is there access to an enterprise-wide healthcare data warehouse? • Can the data be transformed into useful information? • How will data be collected and distributed / shared? – Determine the types of data needed: • Administrative • Clinical • Human resource • Financial – Identify the data owners 6
7. Identify Data Sources • Clinical – Claims – PBM – EMR feeds – Hospital Census – HEDIS reports – State Medicaid data feeds – State All Payer Claims data feeds – Quality of Care / Serious Reportable Events reports 7
8. Identify Data Sources • Organizational resources – Human Resources – Information Technology – Medical Economics / Business Analytics – Finance – NCQA, URAC, other quality survey results • Member/Patient – CAHPS – HOS – Organizational Customer Satisfaction Surveys – Grievances 8
9. Selection Improvement Methodology/Framework – Institute for Healthcare Improvement (IHI) - Triple Aim: • Improve the health of the population; enhance the patient experience of care (including quality, access, and reliability); and reduce, or at least control, the per capita cost of care – IHI Science of Improvement: • A clear, measurable aim • A measurement framework in support of reaching the aim • A clear description of the ideas and how these ideas are expected to impact the results (the causal pathway from changes to desired outcomes) • A clear description of the execution strategy • Dedication to rapid testing (PDSA cycles), prediction, and learning from tests 9
10. Selection Improvement Methodology/Framework • Donabedian Model: – Conceptual model that provides a framework for examining health services and evaluating quality of care – Information about quality of care can be drawn from three categories: structure, process, and outcomes • Structure: The context in which care is delivered including staff, financing, and equipment. • Process: The transactions between members/patients and providers throughout the delivery of healthcare. • Outcomes: The effects of healthcare services on the health status of member/patients and populations 10
11. Selection Improvement Methodology/Framework • Tools: – Plan-Do-Study-Act (PDSA) cycles for small, rapid-cycle tests of change – Adopt learning from variation and heterogeneity: • Use of time-ordered data to detect special cause and improvement • Understanding why results differ by location • Application of behavioral and social sciences – Project Management Methodology • Scrum Methodology / Agile Scrum Methodology – Business Management Methodology • Key performance indicators (KPI) / Key success indicators (KSI) 11
12. Develop Plan • Consider using the 80 / 20 or 90 /10 principle – Identify the clinical processes with the highest variation and highest resource consumption – Identify the members / patients with the highest resource consumption • Reduce duplication – Evaluate current clinical quality activities and determine: • What continues • What does not continue • What can be modified • What has to be added 12
13. Develop Plan • Identify appropriate organizational resources – Are the correct resources in place to support the required activities? – Do the work groups have the right leadership to make change? – What teams are formed and working successfully? – What may need to change? • Gain consensus – Share data / information 13
14. Implement • Sharing the strategy • Unveiling the plan • Question and answer opportunities • Training / Education • Subject matter experts (SMEs) • Continued status updates 14
15. Validate, Course Correct, If Needed • Use the tools – KPI / KSI • Frequent updates – Scrum • What is on track/what is not? – Rapid-cycle testing • What is working what is not – is this validated? – PDSA • Stay the course or make changes • Reevaluate and, based on data, make changes as needed 15
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