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Population Health Research and Evaluation

SIM Data Dashboard

In 2014, the Center for Medicare & Medicaid Innovation awarded Connecticut a four year, $45 million State Innovation Model (SIM) Test Grant. Connecticut’s SIM (CT SIM) worked to improve Connecticut’s healthcare system for the majority of residents by establishing a whole-person-centered healthcare system that aimed to improve community health and eliminate health inequities, ensure superior access, quality and care experience, empower individuals to actively participate in their health and healthcare, and improve affordability by reducing healthcare costs.

Drs. Robert Aseltine of UConn Health and Paul Cleary of Yale University co-directed the CT SIM evaluation to determine its impact on Connecticut’s healthcare consumers, providers, and organizations. The evaluation focused on tracking progress towards the SIM aims and provided opportunities for continuous quality improvement.

The State of Connecticut contracted with UConn Health and Yale University to monitor and report on the process and impact of CT SIM. The evaluation team used data from several Connecticut State Agencies to calculate results. A data dashboard was created to present progress towards meeting the planned CT SIM goals.

The CT SIM Dashboard is a web-based presentation of approximately 30 measures used to monitor and report on the process and impact of SIM. Measure topics included population health, healthcare costs, healthcare delivery, and health insurance transformation. Population health measures, PCMH+, and CCIP were updated yearly. Other measures were updated quarterly.

The dashboard presents overall results for each measure and by categories including age, gender, race/ethnicity, income, insurance payer as the data allows. Overall results are compared to targets to show progress towards meeting the planned CT SIM goals. Targets, future predicted values, were calculated using pre-SIM historical data and trends. A goal of a five percent decrease in predicted values over the five years of SIM programming was set. The decrease was distributed equally over the five year span with the yearly targets set at a one percent decrease per year off of predicted values. Predicted values were used to account for upward and downward trends that were found in many performance measures.

The dashboard allows users to track performance of the measures over time, to assess performance relative to program goals, and to visualize data in custom charts and graphs.

To view dashboard pages, use the menu to the right to explore the health measures being evaluated: Population Health, Healthcare Costs, Healthcare Delivery, Health Insurance Transformation. With the interactive tables and graphs, you can search, sort, and filter results by measure, age, gender, race/ethnicity, income, county of residence, insurance payer and year where data allows. Pop-up bubbles appear throughout the website. Pop-up bubbles provide more information on many features of the dashboard. The dashboard also has many links to external websites that allow you to easily find more information on a particular measure.

As you use the dashboard, you will see these symbols in the graphs:

  Observed value – This value shows the state average for a given measure. It can be presented for overall, subgroup or year.

Target value – This is the level we hope to reach as an outcome of SIM.

* Suppressed value – This value does not meet publication criteria.

Keep in mind that for some measures it is desirable to have the observed value at or above the target value. For other measures, it is better to have the observed value at or below the target value.

The project described was supported by Grant Number 1G1-CMS-331630-01-05 from the Department of Health and Human Services, Centers for Medicare and Medicaid Services. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Department of Health and Human Services, or any of its agencies. The research presented here was conducted by the awardee. Findings might or might not be consistent with or confirmed by the findings of the independent federal evaluation contractor.