Policy Analytics

  • Responding to Federal Register CMS-1715-P request, we provided documentation that the Medicare Spending Per Beneficiary (MSPB) program initiated in 2013 and expanded by IMPACT 2014 supports CMS needs for a rapid start of MIPS Value Pathway (MVP) program.  9-27-19

 

  • Comments to HSCRC Regarding – Developing National Benchmarks for Maryland Hospital Markets. Medicare Spending Per Beneficiary (MSPB) documents wide efficiency performance ranges among benchmark market area hospitals.  Bergen County, NJ at 1.079 was least efficient and Hennepin County, MN at 0.9304 was most efficient.  This suggests potential bias in use of benchmark county data when comparing with Maryland counties.  4-29-19.

 

  • Strengthening Clinician Users of CRISP

Measures or approaches include:  improve homogeneous grouping; develop frailty measure; enhance physician descriptors; develop coordination of care metrics; develop an ECIP data central; automate physician NPI/TIN lookups; retrospective review to identify physicians providing unnecessary patient care; and begin implementation of patient relationship codes.  Establish separate analyses for hospitalists and Primary Care linkages.  12/2018.  Internal RI white paper.

 

  • Maryland Hospital Association – TCOC and Opportunities. The Maryland Agency Model.  Need to identify hospital variance and persistent variances and develop clinical interventions with fidelity and impact.  8-13-18.

 

  • Letter to HSCRC re Methodological Observations on Draft Recommendations of the Maryland Health Services Cost Review Commission regarding Hospital Acquired Conditions for 2018 for Rate Year 2020. We explored the impact of low frequency PPCs – did the decreasing frequency result from changed utilization patterns or improved quality?  January 10, 2018.

 

  • In a DOH Duals ACO (D-ACO) we modeled spillover as a D-ACO program impact. We focused on 2013 Medicare fee for service (FFS) beneficiaries’ transition to Medicaid from Medicare.  Conclusion – We projected spillover from improved Duals ACO management would yield an additional $41,000,000 in savings.  Our numbers are conservative approximations.  We believe spillover is a viable concept and should be refined and included in future duals impact analyses.  February 2017.

 

  • Primary Care – Schumacher DN, Beilenson  PL, Carlson R.  Resourcing Primary Care in An Era of Health Care Change – A White Paper Discussing the Challenges and Solutions to Enhance the Availability and Affordability of Quality Primary Care in the Howard County, Maryland Area.  February 22, 2010.

 

  • Prospective Payment for Psychiatry – Feasibility and Impact (National Policy)  -We investigated the ability of the psychiatric DRGs to predict the hospital length of stay and costs by retrospectively analyzing 8816 charts randomly selected patients from 32 hospitals throughout the United States.

 

  • The psychiatric DRGs reduced the total variance in length of stay by 3.9 percent. Our best alternative grouping – based on major diagnostic categories, whether the patient was transferred from another facility, age, and psychiatric complications and comorbidities – reduced the variance by 7.8 percent.  DRGs do not adequately predict length of stay or costs in psychiatric hospitals.  All models we tested would create large financial “winners” and “losers” and thus introduce inappropriate incentives.   NEJM Med 1986; 315:1331-6.

 

  • Hospital Cost Per Case – Analysis Using a Statewide Maryland Data System -We (Schumacher, Horn, Solnick, Atkinson and Cook) established relationships between hospital cost per case and the independent variables: case mix complexity, case mix severity, factor input prices, and hospital characteristics.  Two hundred and sixteen thousand discharges from Maryland’s acute general hospitals are grouped into 383 Diagnostic Related Groups which are used to compute an information theoretic measure of case mix complexity.  Multiple linear regression equations are developed which predict up to 88% of the variance of between-hospital cost per case.  The most highly significant predictors of cost per case are complexity, patient age, proportion of high-risk patients, average length of stay, and nonphysician salary levels.  Two distinct groups of hospitals, metropolitan and rural, are defined and models are developed for each.  We discuss the implications of these findings for the identification and regulation of unexpectedly high cost hospitals and for prospective cost per case reimbursement.  Medical Care October 1979.