Rethinking Medicare Payment Adjustments for Quality : The Journal of Ambulatory Care Management

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Paying for Outcomes

Rethinking Medicare Payment Adjustments for Quality

Averill, Richard F. MS; Fuller, Richard L. MS; McCullough, Elizabeth C. MS; Hughes, John S. MD

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Journal of Ambulatory Care Management 39(2):p 98-107, April/June 2016. | DOI: 10.1097/JAC.0000000000000137
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THE “MEDICARE Access and CHIP Reauthorization Act of 2015” (HR 2) establishes a Merit-based Incentive Payment System (MIPS) for physicians that includes measures of quality, resource use (efficiency), meaningful use of electronic health records, and clinical practice improvement. The stated intent of MIPS is to move from paying for volume to paying for value. A composite MIPS score will be used to determine physician payment penalties and bonuses. Similar to the Medicare Value-Based Purchasing (VBP) payment adjustment for hospitals and the Medicare Star rating for Medicare Advantage plans, many of the measures in MIPS will likely be measures of adherence to specific clinical and administrative processes that are combined together into a composite measure of performance. The sheer number of process measures and complexity of the methods used to determine an overall composite score makes it difficult for health care delivery organizations to understand and focus on the actions needed to improve “value.” Thus, the attempt to pay for value using process measures may be getting lost in an overly complex attempt to measure value.


There appears to be an emerging consensus that reliance on adherence to care processes is neither effective for measuring value nor effective for controlling expenditures. In his June 2014 testimony before the Ways & Means Health Subcommittee, the Executive Director of MedPAC, Mark Miller, stated: “Current quality measures are overly process oriented and too numerous, they may not track well to health outcomes, and they create a significant burden for providers” (Miller, 2014).

In a letter to the Centers for Medicare & Medicaid Services (CMS) Administrator, the American Medical Association (AMA) stated that the current collect of payment adjustments for quality were “confusing, misaligned and burdensome” to such an extent that “the rules have become so convoluted that no one, including the staff in charge of implementing them, can fully understand and interpret them” (AMA, 2014). The AMA also called on CMS to “simplify and align incentive programs to ensure there is one streamlined process in place.”

The Institute of Medicine (IOM) observed that “thousands of measures are in use today to assess health and health care” but “their sheer number, as well as their lack of focus, consistency, and organization, limits their overall effectiveness in improving performance of the health system” (Blumenthal et al., 2015). A Robert Wood Johnson report on quality performance measures concluded that the focus of performance measures should “decisively move from measuring processes to outcomes” (Berenson et al., 2013).

The use of process measures for payment purposes creates the administrative burden of collecting, reporting, and verifying adherence to the prescribed processes. Even if a process measure is clearly defined and correlates with a desired outcome, the highly vexing problem of defining a common metric for combining the process measures into a composite score remains. For example, in the hospital VBP, the relative importance of diverse measures ranging from the timely removal of urinary catheters to the cleanliness of hospital bathrooms must be established to create the composite measure of overall performance. With process measures that range from clinically significant to micro administrative, the inevitable result is a composite score derived from arbitrary and complex rules that are difficult for health delivery organizations to understand and use for real quality improvement efforts. Thus, adherence to processes dictated centrally by Medicare through payment adjustments is not an effective way of measuring value and controlling expenditures. Despite the apparent failure of process-based payment systems to measure value and control expenditures, process measures may still be a useful internal management tool for individual health care delivery organizations.


Arguably, the Inpatient Prospective Payment System (IPPS) is the most successful health care payment reform ever implemented (Russell & Manning, 1989). It is important to understand and replicate to the extent possible the key lessons that led to the success of IPPS. IPPS was an outcomes-based system. The outcome was the cost of care for each type of patient as defined by the Diagnosis Related Groups (DRGs). A national standard rate of resource use (the price) for each DRG (the product) was established, creating a “product with a price” payment system. If a hospital's production cost was lower than the price it made a profit, but if its production cost was higher than the price, it suffered a loss. Because the unit of payment (the DRG) was clinically meaningful, IPPS linked the clinical and financial aspects of care, giving hospital management and medical staffs a common language to use in managing and controlling cost.

The IPPS financial incentive for efficiency was simple and easily understood and allowed hospitals to respond in the way that worked best in their local environment and for their local community. IPPS did not attempt to dictate how medicine should be practiced by mandating adherence to a multitude of prescriptive process measures. Instead, IPPS recognized that the role of the federal government should be to create incentives to improve efficiency and quality, thereby reducing spending (create value), but not to dictate how care should be delivered in response to that incentive.

The lessons of IPPS are clear: focus on outcomes, set national standards, be clinically meaningful, create the right incentives, and keep it simple.


The “Incentivizing Health Care Quality Outcomes Act of 2014” (HR 5823) represents a significant departure from existing attempts to adjust Medicare payments for quality. It proposes to replace the existing patchwork of process and outcomes-based quality measures used for Medicare payment adjustment with a uniform and comprehensive outcomes-based quality measurement system that would apply to all types of health care delivery organizations including hospitals, Medicare Advantage plans, health homes, and accountable care organizations.

The Outcomes Act specifically focuses on 5 types of quality-related potentially preventable outcomes: complications, admissions, readmissions, emergency department visits, and outpatient procedures and diagnostic tests (collectively referred to as potentially preventable events or PPEs). These 5 PPEs represent the majority of expenditures for preventable health care events. Since failures in quality typically result in a need for more interventions to correct the quality problem, PPEs represent an end manifestation or outcome of an underlying quality problem. The only way a health care delivery organization can improve its PPE performance is to improve quality, efficiency, and care coordination, thereby reducing preventable utilization of services. While IPPS focused on the efficiency with which inpatient hospital services were delivered, the Outcomes Act focuses on the necessity of both inpatient and outpatient services.

The 2012 IOM study, Best Care at Lower Cost, estimated that unneeded services, mistakes, delivery system ineffectiveness, and missed prevention opportunities were leading to $395 billion in annual health care expenditures that could be avoided without worsening health outcomes (IOM, 2012). By focusing on PPEs that are the end result of a quality failure, the Outcomes Act provides comprehensive financial incentives to health delivery organizations aimed at eliminating the avoidable expenditures identified in the IOM report. The refocusing on a few well-defined outcomes in the Outcomes Act is the kind of synchronization and simplification called for by the AMA and represents a shift in payment policy toward actually getting value instead of an increasingly complex attempt to measure value.


Several state Medicaid agencies are in the process of implementing comprehensive outcomes payment reforms that are consistent with the Outcomes Act. Texas Senate Bill 7 was passed in 2011 and established an outcomes payment adjustment across all health care delivery organizations including managed care plans (State of Texas, 2011). Similarly, New York has issued regulations that establish an outcomes-based payment reform (New York State Department of Health, 2010). While some of the implementation details across the Texas and New York reforms may differ, they both focus on the 5 PPEs in the Outcomes Act.

In addition to the comprehensive outcomes payment reforms in Texas and New York, some states have implemented reforms based on a subset of the PPEs. In its first 3 years, a potentially preventable complication (PPC) payment adjustment system in Maryland resulted in a year-on-year drop in the actual complication rate of 7.8%, 10.7%, and 17.7%, respectively, for a 3-year decline of 32.3%. The PPCs used in Maryland provide a comprehensive identification of complication including severe complications such as sepsis and pneumonia (Calikoglu et al., 2012). In its first 2 years, a potentially preventable readmission Medicaid payment adjustment in Illinois enabled hospitals to avoid payment reductions through reducing preventable readmissions (Illinois Department of Healthcare and Family Services, 2014). In its first 3 years, a potentially preventable readmission project by the Minnesota Hospital Association has resulted in a 19% reduction in readmissions (Minnesota Hospital Association, 2015). These state outcomes-based reforms are demonstrating that payment reforms based on outcomes can yield real and sustainable results.


The core objective of the Outcomes Act is to motivate behavioral change that leads to better quality, improved outcomes, and ultimately lower costs. By focusing in on outcomes that are potentially preventable, health care delivery organizations can direct their quality improvement efforts toward problems where quality can actually be improved. A core assumption of the Outcomes Act is that health delivery organizations with a consistently higher risk-adjusted rate of PPEs are more likely to have underlying quality problems that can be identified and corrected.

Because even the best performing health care delivery organizations will have a residual rate of PPEs even when care is optimal, payment adjustments in the Outcomes Act are based on differences in risk-adjusted PPE rates compared with peer organizations. Health care delivery organizations with excess rates of PPEs have a payment penalty imposed, whereas those with lower rates of PPEs receive a payment bonus. As an inherent by-product of existing payment systems, the financial impact of each of the PPEs is known (eg, the medicare severity diagnosis related groups [MSDRG] payment for a readmission, the Ambulatory Payment Classifications (APC) payment for an emergency department visit). This allows the net financial impact of higher or lower rates of PPEs to be quantified in financial terms so that payment penalties and bonuses for a health care delivery organization are proportional to the net financial impact of its overall PPE performance. Having a clear and established financial measure for each of the PPEs allows a direct computation of the composite performance across all the PPEs based on meaningful relative financial measure, thereby eliminating one of the core problems with using process measures.


The Outcomes Act replaces payment adjustments for hospital-acquired conditions (HACs), readmissions, and VBP for hospitals, physicians, and Medicare Advantage plans. These existing payment adjustments for quality are legislatively mandated to produce a specific level of payment reductions. Since the PPE payment adjustments are replacing those quality payment adjustments, the PPE payment adjustments need to produce an equivalent level of payment reductions. The Outcomes Act generates comparable payment reductions by setting target PPE occurrence rates below historical PPE levels. Because the target PPE rates are below existing PPE rates, payment penalties will exceed payment bonuses, resulting in a net reduction in payments. The magnitude of the payment reduction is directly proportional to the targeted decrease in PPE rates (the greater the target PPE rate decrease, the greater the payment reduction).


Because of the comprehensiveness of the Outcomes Act, any simulation of its impact requires beneficiary claims data from fee-for-service and Medicare Advantage for all sites of service. Because of data access restrictions, the full range of data was not available. However, national fee-for-service Medicare hospital data, contained within the MedPAR (Medicare Provider Analysis and Review) file, were available and could be used to simulate the complication portion of the Outcomes Act. The simulation of the inpatient complication portion of the Outcomes Act identified PPCs and attendant risk adjustment (Hughes et al., 2006), based on the methods used in the Maryland payment system (Health Services Cost Review Commission, 2015).


Medicare data from FY 2014 consisting of 13 566 971 inpatient admissions across 3286 hospitals were used for the simulation. Payments were simulated using the 2014 MS-DRG payment weights and base rate ($5370). Adjustments for outliers, indirect medical education, and disproportionate share were omitted. No inflation adjustment was applied to subsequent years.


The national average PPC occurrence rate was computed for each PPC to create a PPC norm. On a risk-adjusted basis, the actual number of PPCs in each hospital was compared with the number of PPCs expected on the basis of the PPC norm. For each hospital, the cost of the difference between the actual and expected number of PPCs was determined using the marginal cost of each PPC (Fuller et al., 2011). Thus, financial impact of excess PPCs is established in direct proportion to the hospital cost associated with the PPCs. The net PPC financial impact for a hospital was then computed by summing the financial impact across PPCs. Good performance (actual number of PPCs less than expected) on some PPCs was allowed to offset poor performance (actual number of PPCs more than expected) on other PPCs. Hospital penalties and bonuses were then established in direct proportion to the net PPC financial impact.

The PPC penalties and bonuses provide the financial incentive for providers to reduce their actual rate of PPCs. This generates 2 types of payment reductions for Medicare:

  • Payment penalties: Setting the target PPC rates below the historical average guarantees that the payment penalties will exceed payment bonuses, resulting in a net payment reduction.
  • Payment reductions due to fewer PPCs: In the context of the Medicare MS-DRG–based payment system, a decline in inpatient complications will also impact MS-DRG payments. As the rate of inpatient complications such as pneumonia and septicemia decline, there would be a corresponding reduction in the MS-DRG severity levels, resulting in lower MS-DRG payments. The avoidance of a PPC does not always lead directly to a drop in MS-DRG payment (eg, if a patient had multiple comorbid conditions, the elimination of a complication may not change the MS-DRG severity level). However, in general, as PPC rates drop, there will a corresponding drop in payments associated with the PPCs that are occurring less frequently.

In addition to the payment implications, every complication avoided lowers direct and indirect hospital costs. Indeed, one study estimated that inpatient complications constitute 9.5% of inpatient costs (Fuller et al., 2009). In the simulation database, total PPC cost equaled 8.6% of total hospital cost, which is consistent with the previously reported results. As a by-product of the payment incentive to lower complications, hospitals will experience lower operating costs.

Simulation results

The results of the simulation are summarized in Tables 1 to 6. The percentages in Tables 1 to 6 represent the percentage of either total Medicare payments or total hospital cost. Each percentage point represents approximately $1.15 billion in Medicare payments or hospital cost.

Table 1:
Percent PPC Payment Penalty Reduction Without Any Hospital Behavioral Response

PPC payment penalties

For the PPC payment simulation, the targeted reduction in PPC rates used to compute the PPC penalties and bonuses was set to a PPC rate reduction of 10%, 20%, 30%, 30%, and 30% below the baseline PPC rates over a 5-year period, respectively. Thus, the payment simulation establishes a target national reduction in PPCs of 30% that is phased in over 3 years and maintained at the 30% level for the last 2 years of the simulation. The baseline PPC rates were based on the PPC rates in the year before implementing the PPC payment adjustment. Absent any behavioral response by hospitals to lower PPC rates, Table 1 contains the percent reduction in payments due to PPC payment penalties.

A targeted PPC rate reduction of 30% would cause payment penalties to exceed payment bonuses by an amount equal to 2.58% of total payments, thereby reducing Medicare payments by that amount. The 30% reduction in PPC rates assumes that there was no behavioral response by hospitals that lowered PPC rates. If the targeted reduction in PPC rates was set to 100% (equivalent to an expected PPC rate of zero), payment penalties would exceed payment bonuses by 8.6% ($9.9 billion), which is equal to the total cost associated with PPCs. Thus, a 10% reduction in PPC rates translates into payment penalties that are equal to one-tenth of the percentage of total cost associated with PPCs. This amount also corresponds to 8.6% of payment since in the aggregate Medicare payments are approximately equal to hospital costs.

Behavioral response

The primary objective of PPC payment adjustments is to provide the incentive for hospitals to lower their PPC rates. To simulate hospital behavioral response to the PPC financial incentives, it was assumed that decreases in actual PPC rates in hospitals would lag behind the targeted PPC reductions and result in hospital cumulative PPC rate reductions of 5%, 10%, 15%, 20%, and 30% over the 5-year period, respectively. Thus, the simulation assumes that hospitals will respond to the payment incentive and reduce PPC rates with a year-on-year PPC rate reduction of 5% in the first 4 years and 10% in the fifth year. Thus, by the fifth year, there is no difference between the targeted and actual levels of reduction in the PPC rates. This behavioral response rate is slightly slower than the actual PPC reductions achieved in the Maryland PPC payment reform, but, by year 5, it achieves the same 30% reduction in PPC rates experienced in Maryland. Table 2 contains the percent reduction in payments due to PPC payment penalties taking into account hospital behavioral response.

Table 2:
Percent PPC Payment Penalty Reduction With Hospital Behavioral Response

Since the base year rates are not recomputed each year, improvements in hospital PPC performance can partially offset the impact of the reductions in the targeted PPC rates. For example, in year 2 of the simulation, the targeted reduction in the PPC rates is 20%, but by year 2, hospitals are assumed to have already achieved a 10% reduction in their PPC rates so that aggregate payment penalties and bonuses are effectively being computed on the basis of a 10% differential between the actual PPC rates in hospitals and the targeted year 2 PPC rates. As a result of the lower differential between the actual targeted PPC rates, the PPC payment penalty is lower than that shown in Table 1. Indeed, the PPC penalty payment reduction begins to decline in year 4 and becomes zero in year 5. In year 5, hospitals have achieved the objective of lowering PPC rates to the 30% reduction target and so hospital bonuses equal hospital penalties and there is no longer any net payment reduction. Thus, the design of the PPC payment adjustment automatically phases out any payment penalties if the national objective of reducing PPC rates by 30% is met.

Impact on MS-DRG payments

The reduction in MS-DRG payment due to lower PPC rates is given in Table 3. As PPC rates decline, the corresponding reduction in the MS-DRG severity levels results in MS-DRG payments being reduced by 0.36% in year 5 when the 30% reduction in PPC rates has been achieved by hospitals. The elimination of all PPCs would reduce total MS-DRG payments by 1.19%. Thus, a 30% reduction in PPC rates translates into an MS-DRG payment reduction of 0.36%, which is equal to 30% of the MS-DRG payment reduction that would occur if all PPCs were eliminated (1.19%).

Table 3:
Percent MS-DRG Payment Reduction

Total PPC-related payment reductions

The sum of the total payment reduction due to the PPC payment reduction penalties and the MS-DRG payment reductions is shown in Table 4.

Table 4:
Total PPC Percent Payment Reductions Due to MS-DRGs and PPC Payment Penalties With Hospital Behavioral Response

Total PPC percent payment reductions due to MS-DRGs and PPC payment penalties peak in year 3 at 1.47% and decline to 0.36% in year 5. The decline is due to hospitals responding to the PPC payment incentive to reduce PPCs, resulting in a decline in the PPC payment penalties.

Impact on hospital costs

Because of the reduction in PPC rates, systemwide hospital operating costs will be lower as shown in Table 5.

Table 5:
PPC-Related Hospital Percent Cost Savings Due to Behavioral Response

By year 5, hospital operating costs will be 2.58% lower, potentially permitting future hospital update factors (inflation adjustments) to be lower while maintaining hospital margins. It is important to note that IPPS was implemented on a budget-neutral basis. All the savings from IPPS were achieved because hospitals dramatically lowered operating costs, allowing a significant reduction in the hospital update factor in subsequent years.

Net impact on hospitals

The difference between hospital cost savings in Table 5 and the reduction in total payments in Table 4 are given in Table 6.

Table 6:
Net Percent Impact of Hospital PPC Payment Reductions and Cost Savings

The net financial impact on hospitals is negative in the first 3 years but becomes positive in years 4 and 5, reaching 2.22% in year 5. Thus, hospital cost reductions will exceed payment reductions as hospitals reduce their rate of PPCs. It should be noted that this result would not be impacted by the inclusion of outliers in the simulation since current outlier payments will typically be lowered by 80% of the averted cost.

Summary of PPC payment simulation

If hospitals achieve the 5-year objective of lowering PPC rates by 30%, cumulative Medicare payments over the 5 years will be reduced by 0.88% ($5.1 billion) and hospital operating cost will be reduced by 1.38% ($7.9 billion). Thus, hospital cost savings will more than offset the PPC-related payment reductions. At the end of 5 years, hospitals will have a cost structure that is 2.58% lower, potentially reducing the magnitude of future inflation increases. But most importantly, patient care has been improved. A 30% decrease in inpatient complications represents a major improvement in quality of care. As demonstrated by the results in Maryland, this level of improvement can be achieved with well-designed payment incentives.


The Outcomes Act requires that savings from programs being eliminated be replaced to be revenue neutral. The current Medicare payment adjustments for inpatient complications are based on 2 legislative mandates:

  • The Deficit Reduction Act of 2005 requires Medicare to eliminate any increase in hospital MS-DRG payments due to the occurrence of certain inpatient complications, referred to as HACs (CMS, 2015b). On a per case basis, HACs are excluded from MS-DRG assignment, thereby lowering payment for some patients. CMS estimates that the per case HAC payment reductions reduce Medicare inpatient hospital payments by 0.02 of a percent (Research Triangle Institution, 2012).
  • Beginning in 2015, the Accountable Care Act of 2010 establishes the Hospital Acquired Condition Reduction Program (HACRP) that requires that the 25% of hospitals with the poorest complication performance have their Medicare payments reduced by 1% (CMS, 2015a). Assuming that the hospitals incurring the 1% penalty have the same characteristics as hospitals overall, the 1% hospital payment reductions will reduce Medicare inpatient hospital payments by 0.25%.

Thus, the overall expected payment reduction for Medicare from the 2 HAC-related payment adjustments is 0.27 of a percent of Medicare inpatient payments. The HACs as currently defined are very narrow in scope and relatively infrequent but vary in response to hospital performance. The HACRP payment adjustment is set as a flat 1% penalty and is therefore out of proportion to the actual financial impact of HACs. The Outcomes Act would repeal both HAC programs. Because the PPCs are a much more comprehensive identification of complications than HACs, only a 3.14% reduction in the PPC rate is necessary to replace the combined HAC 0.27% payment reduction. The PPC payment penalties and bonuses also have the benefit that they are proportional to the financial consequences of higher or lower rates of PPCs. In addition to imposing payment penalties that are out of proportion, the HACRP payment penalties have been shown to be biased against certain classes of hospitals (Rajaram et al., 2015), calling into question the basic design of the HACRP system.

The payment design in the Outcomes Act adheres to the key lessons from the implementation of IPPS. It focuses on outcomes and sets a national objective for reducing the rate of PPEs. As health care delivery organization meet the national objective, the payment penalties are phased out, rewarding improved performance. Because the focus is on the risk-adjusted rates of PPEs, clinically meaningful information that can be used to improve performance is an inherent by-product. The incentives in the PPE payment adjustment are straightforward and avoid the confusing maze of measures in process-oriented systems.

This simulation only focused on the impact of the PPEs. If all 5 PPEs were included in the simulation, the payment and cost reductions would have been substantially larger.

In the initial years of IPPS, the annual hospital update factor was dramatically reduced while hospital margins improved. It was a win-win for Medicare and hospitals. The Outcomes Act has the potential to replicate the IPPS win-win result. As the PPC simulation demonstrated, Medicare can lower payments through PPC payment adjustments, with hospitals more than offsetting the payment reductions through lower internal costs due to reductions in the cost of bed-days and ancillary services that result fewer complications. And most importantly, Medicare beneficiaries win because complication rates have been reduced, thereby improving quality of care.


The Outcomes Act proposes a radical departure from the direction of most attempts to introduce a quality payment adjustment. It proposes to replace the existing patchwork of process and outcomes quality measures with a uniform, coordinated, and comprehensive outcomes-based quality measurement system. The focus on a few outcomes that have a large financial impact has the potential to direct quality improvement effort where the greatest return can be achieved. The Outcomes Act represents a shift in payment policy toward getting value instead of an increasingly complex attempt to measure value. The Act recognizes that the role of the federal government is to create incentives for improved efficiency and quality, thereby creating value, but not to dictate how care should be delivered. The Outcomes Act establishes national outcomes-based quality improvement objectives that are clear and achievable. Early results from state implementations and the results from the PPC simulation demonstrate that Outcomes Act has the potential to simultaneously yield significant savings for Medicare, reduce the operating costs of health delivery organizations, and improve quality.


American Medical Association. (2014). Regulatory penalties tsunami could cut doctors' pay by 13 percent. AMA Wire. Retrieved from
Berenson R., Pronovost P., Krumholz H. (2013). A chieving the potential of health care performance measures. Retrieved from
Blumenthal D., Malphrus E., McGinnis J. M. (2015). Vital SignsCore metrics for health and health care progress. Retrieved from
Calikoglu S., Murray R., Feeney D. (2012). Hospital pay-for-performance programs in Maryland produced strong results, including reduced hospital-acquired conditions. Health Affairs (Project Hope), 31(12), 2649–2658. doi:10.1377/hlthaff.2012.0357
Centers for Medicare & Medicaid Services. (2015a). Hospital-acquired condition (HAC) reduction program. Retrieved from
    Centers for Medicare & Medicaid Services. (2015b). Hospital-acquired conditions (present on admission indicator). Retrieved from
      Fuller R. L., McCullough E. C., Averill R. F. (2011). A new approach to reducing payments made to hospitals with high complication rates. Inquiry: A Journal of Medical Care Organization, Provision and Financing, 48(1), 68–83.
      Fuller R. L., McCullough E. C., Bao M. Z., Averill R. F. (2009). Estimating the costs of potentially preventable hospital acquired complications. Health Care Financing Review, 30(4), 17–32. Retrieved from
      Health Services Cost Review Commission. (2015). Complications: Maryland Hospital Acquired Conditions (MHAC). Retrieved from
      Hughes J. S., Averill R. F., Goldfield N. I., Gay J. C., Muldoon J., McCullough E., Xiang J. (2006). Identifying potentially preventable complications using a present on admission indicator. Health Care Financing Review, (3), 63–82. Retrieved from
      Illinois Department of Healthcare and Family Services. (2014). Potentially preventable readmissions (PPR): State fiscal year (SFY) 2013 reconciliation. Retrieved from
      Institute of Medicine. (2012). Best care at lower cost: The path to continuously learning health care in America. Retrieved from
      Miller M. E. (2014). Report to the Congress: Medicare and the health care delivery system. Retrieved from
      Minnesota Hospital Association. (2015). Reducing avoidable readmissions effectively. Retrieved from
      New York State Department of Health. (2010). Potentially preventable readmissions. Retrieved from
      Rajaram R., Chung J. W., Kinnier C. V., Barnard C., Mohanty S., Pavey E. S., Bilimoria K. Y. (2015). Hospital characteristics associated with penalties in the Centers for Medicare & Medicaid Services Hospital-Acquired Condition Reduction Program. JAMA, 314(4), 375–383. doi:10.1001/jama.2015.8609
      Research Triangle Institution. (2012). 2011 payment savings from MSDRG reassignment. Retrieved from
        Russell L. B., Manning C. L. (1989). The effect of prospective payment on Medicare expenditures. The New England Journal of Medicine, 320(7), 439–444. doi:10.1056/NEJM198902163200706
        State of Texas. S. Bill No. 7, 82 (1) Legislative Session (2011). Retrieved from

        Outcomes Act; pay for performance; payment reform; quality; value based purchasing

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