Article Figures & Data
Tables
- Table 1.
Weighted Number and Percentage of PCPs by Response to Key Survey Questions, by CPC+ Track in 2019
Response Question 1 Question 2 Track 1, % Always or most of the time 78.0 65.2 Sometimes 17.0 33.3 Seldom or never 4.9 1.5 PCPs, No. 2,244 2,273 Track 2, % Always or most of the time 78.4 67.5 Sometimes 16.8 31.5 Seldom or never 4.9 1.1 PCPs, No. 2,418 2,448 CPC+ = Comprehensive Primary Care Plus; PCP = primary care physician; PY 3 = program year 3.
Notes: Source is authors’ analysis of CPC+ PY 3 Physician Survey data. Results weighted using track-specific survey weights including nonresponse weights. The number of respondents differed for the 2 survey questions.
Question 1: When you refer a patient to a specialist, how often do you send the specialist notification of the patient’s history and reason for the consultation?
Question 2: How often do you receive useful information about your referred patients from specialists?
- Table 2.
Association Between Characteristics of Beneficiaries, Primary Care Physicians, and Primary Practice Sites With the PCP Sending or Receiving Information Always or Most of the Time to Specialists, Using Logistic Regression Models
Explanatory Variable Sending Information to Specialist Marginal Effect (P Value) Receiving Information From Specialist Marginal Effect (P Value) Track 1 Track 2 Track 1 Track 2 Characteristics of beneficiaries attributed to primary care physician Percentage of beneficiaries with specific characteristica,b Age category, y <65 0.005 (.09) 0.005 (.06) 0.003 (.35) –0.004 (.18) 65-74 (reference) NA NA NA NA 75-84 0.003 (.09) 0.005c (.01) –0.001 (.68) 0.002 (.37) ≥85 0.001 (.8) 0.001 (.58) 0.003 (.25) –0.001 (.61) Race category White (reference) NA NA NA NA Black 0 (.74) 0.002 (.08) 0.003c (<.001) 0 (.83) All other/unknown –0.001 (.41) 0.002 (.11) 0.001 (.38) 0 (.88) Male 0.002 (.1) 0.001 (.22) 0.004c (<.001) 0 (.9) Original Medicare eligibility categories Age (reference) NA NA NA NA Disability only –0.006d (.03) –0.008c (<.001) –0.002 (.52) 0.003 (.38) ESRD only or ESRD with disability –0.018 (.08) –0.002 (.84) –0.004 (.74) 0.008 (.46) Indicator for dual status 0.003d (.02) 0.001 (.48) –0.001 (.5) 0.001 (.38) Reversed Bice-Boxerman Index ≥0.85e –0.001 (.31) 0 (.93) 0.002d (.03) 0.002 (.06) Average beneficiary risk for subsequent expendituresa HCC scoref –0.184 (.3) 0.027 (.88) 0.203 (.37) 0.246 (.2) Characteristics of primary care physician Age categories, yg ≤50 (reference) NA NA NA NA >50 –0.012 (.51) 0.037 (.07) 0.1c (<.001) 0.069c (<.001) Malef –0.025 (.46) –0.033 (.34) –0.064 (.1) 0.031 (.42) Specialtyg Family medicine (reference) NA NA NA NA General practice –0.054 (.6) –0.133 (.5) 0.045 (.64) –0.189 (.15) Internal medicine –0.001 (.95) –0.047 (.05) –0.057d (.04) –0.032 (.21) Time in direct patient careh –0.095 (.27) –0.04 (.61) 0.053 (.57) 0.043 (.63) Comprehensiveness measures Range of services measure: assesses the comprehensiveness of services that a physician provided to Medicare fee-for-service beneficiariesh 0.007 (.53) 0.005 (.61) 0.01 (.43) –0.007 (.57) New problem management measure: assesses extent to which a physician manages patients’ new symptoms or problems instead of referring them to (or the patients seeking) a specialisth –0.296 (.17) 0.187 (.35) –0.148 (.55) 0.554d (.02) Involvement in patient conditions measure: assesses extent to which a physician is involved in the care of the broad range of their patients’ health conditionsh 0.236d (.03) 0.107 (.28) 0.118 (.35) 0.009 (.94) Characteristics of primary care practice site, including market characteristics CPC+ practice site (vs comparison site)i 0.036 (.07) 0.013 (.5) 0.026 (.24) 0.005 (.82) Practice site has ≥1 NP or PAh –0.004 (.87) –0.016 (.46) –0.012 (.64) 0.036 (.17) Number of total practitioners (any specialty)i,j –0.002d (.05) 0 (.77) –0.001 (.56) –0.002d (.05) Number of practitioners at practice site with primary care specialtyi,j Small (1-2 primary care practitioners) (reference) NA NA NA NA Medium (3-5 primary care practitioners) 0.021 (.46) 0.012 (.69) –0.042 (.19) –0.026 (.43) Large (≥6 primary care practitioners) 0.016 (.67) –0.001 (.99) –0.014 (.74) 0.002 (.97) Practice site is multispecialty vs primary care onlyi –0.038 (.21) –0.032 (.25) 0.07d (.03) 0.016 (.6) Hospital or health system owns the practice sitei –0.029 (.17) –0.046d (.02) 0.051d (.04) 0.12c (<.001) Whether practice participated in an a Shared Savings Program ACOk Did not participate in Shared Savings Program (reference) NA NA NA NA Shared Savings Program Track 1 –0.044d (.03) –0.05d (.02) –0.015 (.52) –0.019 (.42) Shared Savings Program Track 2 or 3 0.001 (.98) –0.006 (.89) –0.016 (.76) –0.062 (.21) Primary care transformation experience: NCQA, TJC, AAAHC, URAC, or state medical-home recognition status (whether practice is in a medical home) or participated in CPC Classic or MAPCPl 0.017 (.42) 0.013 (.57) 0.036 (.13) 0.02 (.42) Meaningful EHR usem Did not meet meaningful use criteria (reference) NA NA NA NA Met meaningful use criteria 2011-2012 0.008 (.82) 0.027 (.66) 0.06 (.17) 0.065 (.26) Met meaningful use criteria 2013-2015 0.048 (.25) 0.114 (.07) 0.062 (.21) –0.008 (.91) Practice after hours (number of weekdays practice is open after 5 pm and whether practice is open Saturday or Sunday)i 0.004 (.45) –0.004 (.46) 0.004 (.52) 0.008 (.15) US Census regioni,n Northeast (reference) NA NA NA NA Midwest 0.042 (.2) –0.036 (.22) 0.016 (.63) 0.097c (.01) South 0.125c (<.001) 0.022 (.6) –0.094 (.08) 0.07 (.17) West 0.053 (.18) 0.01 (.79) 0.038 (.37) 0.044 (.32) Median household income of countyo 0 (.28) 0c (.01) 0 (.48) 0 (.44) Practice site in a county Health Professional Shortage Area in 2015-2016o 0.012 (.92) 0.107 (.14) 0.159d (.04) –0.068 (.46) Urbanicity of practice site countyo Urban (reference) NA NA NA NA Rural 0.071 (.1) 0.092d (.04) –0.051 (.36) –0.02 (.71) Suburban 0.036 (.27) 0.068d (.03) –0.011 (.76) 0.038 (.26) Percentage of adults age ≥25 years in the county with a degree from a 4-year collegeo –0.002 (.3) 0.002 (.38) 0.002 (.51) –0.002 (.38) Percentage of county’s population in povertyo –0.003 (.56) –0.005 (.32) 0.003 (.55) –0.009 (.07) Number of hospital beds per 1,000 in practice site countyo Quartile 1 (reference) NA NA NA NA Quartile 2 –0.009 (.76) –0.009 (.75) –0.027 (.37) –0.003 (.92) Quartile 3 –0.041 (.2) –0.002 (.94) –0.058 (.1) 0.025 (.52) Quartile 4 –0.056 (.19) –0.093d (.02) –0.094d (.04) 0.02 (.67) 2015 Medicare Advantage penetration rate in countyo 0.002d (.05) 0.001 (.35) 0.002 (.07) 0.004c (<.001) Number of assigned beneficiariesa 0 (.09) 0 (.38) 0 (.4) 0 (.49) Hospital Referral Region Price Index in 2015: measures actual per capita costs on Medicare Parts A and B relative to standardized per capita costsp –0.081 (.68) –0.032 (.88) –0.12 (.6) 0.37 (.13) Ratio of PCPs to total physicians in the countyo 0.299 (.14) 0.235 (.2) 0.357 (.06) 0.396d (.02) Number of practitioners per 100,000 residents in the county in 2016o 0 (.15) 0 (.55) 0c (.01) 0c (<.001) PCPs, No. 2,244 2,418 2,273 2,448 Adjusted R-squared 0.12 0.11 0.06 0.07 AAAHC = Accreditation Association for Ambulatory Health Care; ACO = accountable care organization; CMS = Centers for Medicare and Medicaid Services; CPC+ = Comprehensive Primary Care Plus; EHR = electronic health record; ESRD = end-stage renal disease; HCC = hierarchical condition category; MAPCP = multipayer advanced primary care practice; NA = not applicable; NCQA = National Committee for Quality Assurance; NP = nurse practitioner; PA = physician assistant; PCP = primary care physician; TJC = The Joint Commission; URAC = Utilization Review Accreditation Commission.
Note: Models estimated using logistic regression, where dependent variable is 1 if survey response is “Always or most of the time” and 0 otherwise.
↵a All beneficiary characteristics come from Medicare fee-for-service claims and enrollment data. They are measured as of the end of 2016 (before CPC+ began).
↵b The following individual HCCs were also included in the regression model but are not reported: HCC 8-Metastatic Cancer/Acute Leukemia; HCC 18-Diabetes with Complications; HCC 21-Malnutrition; HCC 22-Morbid Obesity; HCC 23-Endocrine/Metabolic Disorders; HCC 40 or 47-Rheumatoid Arthritis; HCC 46 or 48-Severe Hematological Disorders; HCC 54 or 55-Drug/Alcohol Psychosis or Dependence; HCC 57 or 58-Schizophrenia or Major Depressive Disorders; HCC 70 or 71-Quadriplegia or Paraplegia; HCC 80 or 82-Coma; HCC 85-Congestive Heart Failure; HCC 86, 87, or 88-Acute Myocardial Infarction; HCC 96-Specified Heart Arrhythmias; HCC 99 or 100-Stroke; HCC 106-Atherosclerosis of the Extremities; HCC 107 or 108-Vascular Disease with Complications; HCC 111-Chronic Obstructive Pulmonary Disease; HCC 157 or 158-Pressure Ulcer of Skin with Necrosis; HCC 173-Amputations; HCC 186-Organ Transplant.
↵c Statistically significant at the .01 level.
↵d Statistically significant at the .05 level.
↵e The reversed Bice-Boxerman Continuity-of-Care Index measures care fragmentation by capturing the number of practitioners providing ambulatory services to a beneficiary and the percentage of care each practitioner provides. Scores ≥0.85 indicate highly fragmented care.
↵f HCC scores are a measure of risk for subsequent expenditures. The Centers for Medicare and Medicaid Services calculates them such that the average for the Medicare fee-for-service population nationally is 1.0. A patient with a risk score of 1.30 is predicted to have expenditures that would be approximately 30% greater than the average, whereas a patient with a risk score of 0.70 is expected to have expenditures that would be approximately 30% less than the average. In our regression model, we also included the percentage of attributed beneficiaries with an assigned HCC score that is derived from CMS’ new enrollee model.
↵g Data source: 2017 Medicare Data on Provider Practice and Specialty.
↵h Data source: 2017 Medicare claims data.
↵i Data source: 2016 SK&A.
↵j Data source: 2016 National Plan and Provider Enumeration System.
↵k Data source: 2016 Master Data Management system 2016.
↵l Data sources: 2016 NCQA, 2016 TJC, 2016 AAAHC, 2016 URAC, state-specific sources 2016; CPC+ data; CMS 2016.
↵m Data source: CMS 2016.
↵n The mapping of 3 states to US Census regions was slightly modified for the selection of a comparison group for the CPC+ evaluation to more closely mirror the CPC+ regions’ market characteristics.
↵o Data source: 2016 Area Health Resource File.
↵p Data source: 2015 Medicare Geographic Variation data.
