Treatment cost estimator carefirst

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We then used propensity scores to match panels based on their cost and quality performance in the commercial program data supplied by CareFirst , their size and ownership, and the demographics, service use, and spending of their Medicare patients. We constructed 6 outcomes from Medicare claims and enrollment data. Three outcomes measured quality-of-care processes: 1 whether patients hospitalized in a quarter had all of their stays followed by an ambulatory care visit within 14 days, 2 whether patients with diabetes received 4 recommended processes of care in a year eye examination, hemoglobin A 1c test, lipid test, and nephropathy screen , and 3 whether patients with ischemic vascular disease received a recommended lipid test in a year.

We selected these quality-of-care measures because CareFirst incentivized improvements in chronic illness care through payments and because they are consistent with recommendations for medical home evaluations. The other 3 measures, defined quarterly, were 1 hospital admissions all-cause and ambulatory-care sensitive , 2 outpatient ED visits, and 3 total Medicare Part A and B spending.

We implemented the difference-in-differences design using multivariate linear regressions for all outcomes. The independent variables were indicators for each measurement interval quarter or year, depending on the outcome ; fixed-effects dummy variables for each panel; interactions between each interval during the intervention period and panel participation in program these interactions were the difference-in-differences estimates ; and beneficiary characteristics defined at the start of either the baseline period for observations in the 1-year baseline or intervention period for observations in the 2.

Beneficiary characteristics included age, sex, original reason for Medicare entitlement disability or old age , Hierarchical Condition Category HCC risk score, and the presence of select chronic conditions. We averaged the quarterly difference-in-differences estimates over specific quarters to test the study hypotheses. The regressions used robust standard errors, clustered at the patient level, and panel fixed effects to account for panel-level clustering.

We conducted all analyses in Stata statistical software version We expected impacts to be larger for this group because 1 their risk of acute care was higher, creating more room for improvement, and 2 a larger fraction The panels' attributed Medicare patients were, on average, The intervention panels were similar within 0. For admissions, outpatient ED visits, ambulatory follow-up care after discharge, and spending, the panels also showed similar trends during the 4 baseline quarters Figure and eFigures in the Supplement.

However, intervention panels, on average, had more PCPs 9. The intervention and comparison panels also differed by more than 0. However, these differences were not large or statistically significant. All 14 intervention panels participated throughout the 2. Patients receiving these services were 1. Nurses coordinated care for patients for 9.

CareFirst paid incentives to panels in each of the 3 performance years The 5 program consultants provided technical assistance to panels, on average, 3 times per month.

Abbreviations: IQR, interquartile range; PCP, primary care practitioner physicians, nurse practitioners, and physician assistants. For all other outcomes, for all Medicare patients as well as the high-risk subgroup, the intervention was not associated with any statistically significant changes in outcomes Table 3 and Table 4.

The difference-in-differences estimates for all Medicare patients in months 13 to 30 were 1. The means for the treatment and comparison groups tracked each other closely for other outcomes as well eFigures in the Supplement.

This study assessed the impacts of a medical home initiative focused on care coordination for high-risk patients and strong financial incentives to panels that meet cost and quality targets.

Primary care practitioners and CareFirst-hired nurses coordinated care for high-risk patients as planned, and CareFirst provided technical assistance and paid outcome incentives awards as planned. The difference-in-differences estimates show that the program did not measurably improve quality-of-care processes or reduce service use, for all Medicare patients or for the high-risk subgroup with larger expected impacts.

As a result, the program did not produce any Part A and B savings to offset the cost of the program. However, the comparison group showed similar trends, suggesting they were driven by outside forces. Medicare hospitalizations rates in Maryland have declined in the past decade, as they have nationally.

Furthermore, cost growth has been modest in the past 5 years. These trends may reflect a combination of improved patient health, hospital responses to incentives to reduce readmissions, and a shift of hospital services from inpatient to outpatient settings. Using the same time period and data but different sample definitions and regression specifications, Afendulis et al found that the program did not generate net savings but did reduce medical spending enough to fully offset the fees and bonuses CareFirst paid to participating panels.

Our study differs from these earlier studies in 3 ways. First, we estimated impacts on the Medicare FFS population, not the commercial population. Second, we estimated impacts for the 14 of the commercial panels that CareFirst selected for extension to Medicare, whereas the earlier studies examined effects for all commercial panels.

Afendulis et al cited lack of practitioner engagement as 1 likely explanation for smaller than expected impacts for the commercial patients, but this is unlikely to be the explanation in our study because CareFirst selected 14 panels that were among the most engaged for extension to Medicare. Finally, the care coordination component reached a larger fraction of the Medicare population than of the commercial population in earlier studies.

Three potential limitations may help explain why the CareFirst program did not measurably reduce medical spending for Medicare patients but did for commercial patients. First, CareFirst used the same algorithm for identifying high-risk Medicare patients as it does for identifying high-risk commercial patients.

Therefore, targeting these top 2 bands gives clear direction to nurses about where to focus their efforts. Furthermore, the care coordination services may not be sufficiently tailored to Medicare patients. For example, nurses contacted patients almost exclusively by telephone, whereas previous reviews have found that, for Medicare patients, frequent in-person contact may be critical for reducing hospitalizations. Second, CareFirst used commercial claims data to classify practitioners as high, medium, or low cost, and program consultants encouraged PCPs to refer Medicare patients more often to lower-cost specialists.

However, physicians considered low cost based on commercial claims may in fact be medium or high cost for Medicare patients, given that price differences can drive variation in commercial spending while volume differences drive spending variation in Medicare where prices are set administratively.

Finally, by using a benchmark 2. Using a benchmark closer to the observed spending growth in Maryland may have signaled to panels the need to continue to adapt their interventions for Medicare patients to meet program aims. Our difference-in-differences estimates also found the program was associated with a statistically significant reduction in the percentage of people with diabetes receiving recommended care.

While surprising, this may be due to PCPs shifting their attention from lower to higher-risk patients. Rosenthal et al also found that a medical home initiative reduced diabetes processes of care, noting that a possible cause was diverting attention away from screening. However, it is also possible that the treatment panels performed unusually well in the baseline year and would have regressed closer to mean performance across all panels in the intervention period even without the intervention.

Our study has 3 main limitations. First, because the design is not experimental, unobservable differences between the intervention and comparison panels may mask program impacts. However, we expect any spillover to be small because the core of the intervention is care coordination for individual patients and care for 1 patient is unlikely to substantively change care for others. The contrast with more favorable results for commercial patients suggests several ways the program could be further adapted to the Medicare population.

These include refining the targeting algorithm to better identify those who could benefit from care coordination, adopting care coordination strategies like in-person contacts shown to be effective for Medicare patients, and tiering specialists on episode costs for Medicare, rather than commercial, patients. Furthermore, using local benchmarks of actual spending growth to calculate panel performance would improve signals to panels about when they need to refine their approaches.

Additional testing would be needed to determine whether these or other changes would lead to a more successful medical home program for Medicare patients. Published online Sep 5. Prepublished online Jul Greg Peterson. Author information Article notes Copyright and License information Disclaimer. Corresponding author. Corresponding Author: G. Received Feb 21; Accepted May Copyright American Medical Association. All Rights Reserved.

See commentary " The Importance of Independent Evaluation. Findings In a difference-in-differences analysis with 52 intervention practices and matched comparison practices, the program was not associated with outcome improvements for Medicare patients. Meaning This medical home model needs further adaptions and testing before being scaled broadly for Medicare patients.

Abstract Importance CareFirst, the largest commercial insurer in the mid-Atlantic Region of the United States, runs a medical home program focusing on financial incentives for primary care practices and care coordination for high-risk patients. Main Outcomes and Measures Hospitalizations all-cause and ambulatory-care sensitive , emergency department visits, Medicare Part A and B spending, and 3 quality-of-care process measures: ambulatory care within 14 days of a hospital stay, cholesterol testing for those with ischemic vascular disease, and a composite measure for those with diabetes.

Results On average, each of the 14 intervention panels had 9. Introduction Payers and primary care practitioners PCPs physicians, nurse practitioners, and physician assistants have embraced the patient-centered medical home as a way to improve health system performance.

Implementation Measures CareFirst provided names and dates of Medicare patients receiving care coordination services. Comparison Panels Because we aimed to estimate the marginal effect of extending the commercial program to Medicare patients, we selected the 42 comparison panels from the participating in the commercial program in , but not its extension to Medicare patients.

Measures of Quality, Utilization, and Spending We constructed 6 outcomes from Medicare claims and enrollment data. Statistical Analysis We implemented the difference-in-differences design using multivariate linear regressions for all outcomes. Results Panel Characteristics at Baseline On average, each of the 14 intervention panels had 9. Table 1. Baseline Characteristics of Intervention and Comparison Panels.

Open in a separate window. First, we calculated values for each panel, for example the percentage of each panel's attributed patients who were female. Mean Unadjusted Number of All-Cause Hospitalizations per Medicare Patients, by Group and Quarter The vertical blue line marks the break between the baseline and intervention periods.

Program Implementation All 14 intervention panels participated throughout the 2. Table 2. Measures of Program Implementation. Table 3. Table 4. The other outcomes do not rely on ICD codes and so could be constructed through the end of the program. Discussion This study assessed the impacts of a medical home initiative focused on care coordination for high-risk patients and strong financial incentives to panels that meet cost and quality targets.

Limitations Our study has 3 main limitations. Notes Supplement. References 1. Patient-centered medical home initiatives expanded in providers, patients, and payment incentives increased. Pay Now. As you transition to Medicare, we hope you'll Stay with Blue and get Medicare coverage from a company you trust.

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The new open enrollment guides still contain the critical information that members need to confidently choose their health plan and benefits. Content is concise and intended to inform members about the offerings that CareFirst has without overwhelming them with too much information. You can add and remove select pages, customize open enrollment dates, benefits and information about how to enroll in a health plan.

Additionally, you have the added flexibility to customize messaging and add limited amounts of additional content. Yes, information and training will be available for internal sales, brokers and employers. Recorded webinars will be available on this page starting Friday, September Standard operating procedures will also be available this fall. If you need additional support using the tool and new interface, email LRC.

Custom enrollment guides are guides built manually by the CareFirst marketing team that offer additional opportunities to update messaging and content based on employer needs.

Custom guides are usually available for self-insured accounts, municipal accounts or accounts with complex and customized benefits. CareFirst will draft custom enrollment guides and ensure all information is complete and accurate.

To create a guide, CareFirst will need to know the following:. Custom enrollment guides generally take four weeks to complete and deliver in digital format. Print on demand enrollment guides are available digitally on the same business day.

For support using the print on demand system, email LRC. If a group has a mix of standard and custom plans you will need to manually enter all medical plans in the open enrollment guide. We plan to make additional automation enhancements in Only standard medical plans can be automatically populated.

Test drive the all-new open enrollment experience now. Resources for Your Convenience Webinars Ready to build open enrollment guides suited to your customers? Health Plan Decision Guide Training. Generally easy to use, straightforward and relatively quick process. When it comes to health insurance, there are a few factors that can affect your costs.

Your age, family size and where you live can all play into the amount you pay for your health insurance coverage. Your age : premiums can be up to 3 times higher depending on your age. Typically, older people pay more than younger ones. Location : where you live can have an impact on your health insurance costs because of local competition, state rules and cost of living. Pre-existing conditions : all health plans must cover treatment for pre-existing conditions once coverage starts.

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To create a guide, CareFirst will need to know the following:. Custom enrollment guides generally take four weeks to complete and deliver in digital format.

Print on demand enrollment guides are available digitally on the same business day. For support using the print on demand system, email LRC. If a group has a mix of standard and custom plans you will need to manually enter all medical plans in the open enrollment guide.

We plan to make additional automation enhancements in Only standard medical plans can be automatically populated. Test drive the all-new open enrollment experience now. Resources for Your Convenience Webinars Ready to build open enrollment guides suited to your customers? Health Plan Decision Guide Training. Generally easy to use, straightforward and relatively quick process. Easy options. Expand All Collapse All When will the new open enrollment changes take place? If the guides are shorter than the previous versions, what information was taken out?

What customizations are available with the new enrollment guides? Will there be training about the new open enrollment print experience? What is the different between custom open enrollment guides and print on demand guides?

What information do we need to create the new enrollment guides for accounts who receive customized materials? What is the turnaround time to create new enrollment guides?

If I have questions or need support, who can I reach out to? If a group has a mix of standard and custom medical plans, can I automatically populate the standard plans and manually enter the custom plans? Are all standard benefits able to be automatically populated into the open enrollment book? Even after understanding the basics of health insurance , it can be confusing to determine how the health plan you choose impacts your out-of-pocket costs.

This makes it tough to estimate and budget for health care expenses. To help you choose the best health plan for your budget and your needs, it is important to understand a bit about health insurance.

This graphic explains how health insurance works. Some key terms are also defined below the graphic. Premium : this is the amount you pay each month for your health insurance coverage.

Your premium does not count toward your deductible or out-of-pocket maximum. Deductible : this is the amount of money you must pay for health care services each year before the plan will start paying for all or part of the services. The plan will pay percent of your covered medical expenses. When it comes to health insurance, there are a few factors that can affect your costs.

Your age, family size and where you live can all play into the amount you pay for your health insurance coverage. Your age : premiums can be up to 3 times higher depending on your age. Typically, older people pay more than younger ones.