Quality

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MassHealth P4P: Did it work?

August 2, 2011 in Hospitals, Medicaid, P4P, Quality | 1 comment

Massachusetts’ Medicaid program instituted a pay-for-performance program in 2008.  Did it work?  According to this paper, the answer is no.

MassHealth P4P Background

The MassHealth pay-for-perfrmance P4P program was implemented in 2008.  At first the program was implmented using a P4P structure for pneumonia and pay-for-reporting for surgical infection prevention (SIP) and transitioning to P4P for both conditions in 2009. The program measures and incentivizes hospital quality for a subset of MassHealth [Massachusetts Medicaid program] patients who are enrolled in plans that directly bill MassHealth.

The Measures

For pneumonia:

  • oxygenation assessment,
  • blood culture performed in emergency department before first antibiotic received in hospital,
  • adult smoking cessation advice and counseling, initial antibiotic received within 6 hours of arrival, and
  • appropriate antibiotic selection in immunocompetent patients.

For Surgical Infection Prevention (SIP):

  • prophylactic antibiotic within 1 hour of surgical incision,
  • appropriate antibioticselection for surgical prophylaxis, and
  • prophylactic antibiotic discontinuedwithin 24 hours after surgery end time.

Evaluating Hospital Performance

The MassHealth P4P followed the Hospital VBP Report to Congress. Hospital performance on individual measures is aggregated to create a composite score; this composite score then is used to indicate the share of the bonus paymen that each hospital receives. More information on the Hospital VBP Report to Congress can be found here.

Identification Strategy

“We do not observe the quality of care provided to Medicaid patients in Massachusetts and other states, and instead we observe the quality provided to patients from all payers. Our identification strategy assumes that the financial incentives of the MassHealth program, which are based on quality performance
for only a subset of MassHealth patients, are reflected in the quality of care received by all patients.”

The authors control for:

  • Observed and unobserved hospital characteristics which remain fixed over time (i.e., fixed effects)
  • A secular trend in quality for each hospital (i.e., using a hospital-specific time trend)
  • Hospital case mix measured by a “difficulty index” to identify cases where hospitals choose patients selectively after P4P was implemented
  • In one sensitivity analysis, the authors use propensity scoring, nearest neighbor, one-to-one matching without replacement to create a sample of non-Massachusetts hospitals similar to those in Massachusetts. Hospitals were matched based on ownership, nuber of beds, urban/rural status, share of Medicare patients, and share of Medicaid patients.
  • The authors also test if hospitals with more Medicaid patients are more likely to have a larger increase in quality.

Evaluating Hospital Performance

The authors find that the MassHealth P4P has little effect on quality. “Estimates from our preferred specification, including hospital fixed effects, trends, and the control for measure completeness, indicate small and nonsignificant program effects for pneumonia (−0.67 percentage points, p>.10) and SIP (−0.12 percentage points, p>.10). ” The result could be due to the fact that P4P has, in actuality, no effect on quality. On the other hand, by using hospital-specific time trends, there may be little variation in quality over time to capture quality improvements after the P4P implementation.

Source

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Dose Chek

July 14, 2011 in Pharmaceuticals, Quality | No comments

How do you know if a patient is receiving the correct dose?  Better yet, how can you check if your entire patient panel is on the right dosage?

Although identifying the ‘right’ dosage is difficult, it is much easier to see if your patients are on the wrong dosage.  Medi-Span’s Dose-Chek data provides information on the following:

  • Screening for inappropriate daily dose
  • Screening for individual doses that exceed maximum recommended values
  • Screening for inappropriate duration of therapy

The Dose-Chek data describing minimum, usual, and maximum daily dosages, as well as maximum individual dosages for a drug product. Because appropriate dosage varies by patient type, Dose-Check has this information for normal adult, geriatric, pediatric, and infant patients in their database.

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The History of Home Health Quality Measurement

December 23, 2010 in Home Health, Quality | 2 comments

Today I will discuss the evolution of home health care measures over the past 15 years. The majority of the content comes from an article by Robert Rosati (2009).

Timeline

Here is a link to a TIMELINE that briefly summarizes the key developments in home health care quality measurements.

Risk Adjustment

From the early development of OASIS, consideration was given to developing case mix or risk-adjusted outcome measures. Patient outcomes are adjusted based on start or resumption of care assessment information. For example, a statistical model was developed to risk adjust the improvement in dressing the lower body based on whether the patient lives alone, receives assistance provided by a caregiver, level of functional status at the start of care, and the presence of other specific clinical conditions [ICD-9 codes]…All 41 outcomes that can be generated from OAASIS have separate risk adjustment models.” Despite the sophistication of the risk adjustment models, these mechanisms only explain variance from 10% to 27%.

Case Mix Adjustment

As part of the home health, prospective payments are adjusted based on the severity of the episodes. These episodes are adjusted for a number of factors. First, the adjustments take into account the clinical and functional status of the patient. Next the episodes are adjusted for service use. Service use before 2008 consisted of whether the patient is expected to receive physical or occupational therapy visits during a home health episode of care.

In 2008 there were major changes to the payment system, including the case mix adjustments. “In 2008, there was a major revision of Prospective Payment System (PPS) with adjustments for early versus late episodes if patients remain open for extended periods (adjustment applies for the third or later contiguous 60-day episodes for a patient) and the amount of therapy services provided to a patient.” The number of case mix adjustment categories changed from 80 before 2008 to 153 in 2008. Now, “the range in an average payment from the top categories (~$8,000) to the bottom (~$2,000) is substantial.”

Quality for Public Consumption

Although public reporting measures were a subset of the home health quality metrics CMS tracked through OASIS, there were some differences. For instance, the phrasing of the measures differed. “Improvement in ambulation and locomotion was changed to percentage of patients who get better at walking and moving around on the CMS Web site.”

The NQF’s also released a report describing its efforts to develop consensus standards for home health care.

Source:

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Quality of Care Measures Across Settings

November 15, 2010 in Medicare, Quality | No comments

How well does Medicare measure quality?  Not very well, especially across different treatment settings.  According to a RAND report:

“Only 10 clinical conditions are addressed by reporting programs for more than one setting. Three clinical conditions are included in programs for three settings: (1) acute myocardial infarction, (2) perioperative/surgical care, and (3) urinary incontinence. Seven conditions are included in programs for two settings: (1) back pain, (2) community acquired pneumonia, (3) depression, (4) end stage renal disease, (5) heart failure, (6) pain, and (7) prevention.”

By not tracking quality measures across providers or treatment settings, physician have less of an incentive to maintain quality across the patient’s full continuum of care.

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VBP Design Choices

November 8, 2010 in P4P, Quality | 3 comments

“Pay-for-performance (P4P) is one of the primary tools used to support healthcare delivery reform. Substantial heterogeneity exists in the development and implementation of P4P in health care and its effects.” Today, I review a paper which summarizes evidence, obtained from studies published between 1990-2009, concerning P4P effects.

Measure Effectiveness

Which types of measures produce the biggest change in physician behavior. The authors’ literature review reveals the following:
“The effect of P4P on non-incentivized quality measures varied from none to positive.  However, one study reported a declining trend in improvement rate for non-incentivized measures of asthma and CHD after a performance plateau was reached.”

In addition, process measures were more effective in changing physician behavior than outcome measures. Intermediate outcome measure effect of provider behavior was between the process and pure outcome measures. Among these measure types, programs where providers were involved in the VBP implementation lead to larger gains in outcomes. The authors do not mention if this was because of easier-to-game measure selection by providers or if this represented actual improvement. Providers can also game the system by declaring a patient ineligible for certain measures. “Gaming by over exception reporting and over classifying patients was kept minimal, although only three studies measured gaming specifically (e.g., 0.87% of patients exception reported wrongly). Therefore, there is limited evidence that gaming does occur with P4P use, although it is not clear what is the incidence of gaming without P4P use.”

The most important factor may be whether the providers are aware that a P4P program has been put in place. “[S]tudies found positive P4P effects (5 to 20% effect size) with programs that fostered extensive and direct communication with involved providers.”

Payment Size and Type

The authors surprisingly found limited impact of P4P payment size on physician behavior. This may be due to the fact that in markets with payer fragmentation, even large P4P amounts will make up a small share of any one physician’s income. Generally, giving positive rewards produced better outcomes than programs with winners and losers, but the authors claim this finding is far from robust.

Payments targeted to organizations seemed to less effective than those targeted at individual providers, but programs aimed at either tended to produce positive results. “A combination of incentives aimed at different target units was rarely used, but did lead to positive results.”

Access and Equity

Read the rest of this entry »

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Hospital Quality

July 27, 2010 in Hospitals, Quality | 1 comment

The HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) is a standardized survey instrument and data collection methodology for measuring patients’ perceptions of their hospital experience.  HCAPHS is the first national standard for collecting and reporting hospitals quality data.

The survey asks discharged patients 27 questions about their recent hospital stay.  The survey is administered to a random sample of adult patients across medical conditions between 48 hours and six weeks following discharge.

Although CMS publicly reports the results of the HCAPHS survey, the sample is not restricted to Medicare beneficiaries.  However, hospitals are allowed to collect their own data which begs the question of whether they manipulate the data.  For instance, they may prefer worse satisfaction scores to have a low baseline or prefer high satisfaction scores to attract more patients.  Although I do not know if this occurs, one could envision a patient conveniently being dropped from the survey if they give the hospital a bad review.

The number of hospitals that publicly report HCAHPS results has increased from 2,521 in March 2008, to 3,711 in March 2009.

Timeline:

  • 2002.  CMS partnered with the Agency for Healthcare Research and Quality (AHRQ), another agency in the federal Department of Health and Human Services, to develop and test the HCAHPS survey.
  • May 2005.  National Qualify Forum endorses HCAHPS
  • December 2005.  Office of Management and Budget (OMB) approves HCAHPS
  • October 2006.  CMS implements HCAHPS survey
  • July 2007.  The Deficit Reduction Act of 2005 requires IPPS hospitals to submit HCAPHS data to receive full IPPS annual payment update.  Non-IPPS hospitals, such as Critical Access Hospitals, may voluntarily participate in HCAHPS.
  • March 2008. CMS publicly reports HCAHPS survey results on Hospital Compare.

Source: HCAHPS Fact Sheet, March 2009.

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Applying the Toyota Production System to Healthcare

May 21, 2010 in Books | 4 comments

Until their most recent quality stumbles, Toyota’s production techniques were the darlings of the management consulting world.  The Toyota process is embodied by the concept of kaizen, a Japanese notion of continuous improvement. The latest gurus have even applied the production techniques to the health care arena (see Designed to Adapt). A Health Affairs article by John Toussaint (2009) shows how Wisconsin has used Toyota-style production techniques to improve quality.

Some of the problems an improved production process could solve include:

  • A large fraction of steps in the health care process have no apparent value for the patient.  Touissaint estimates that this figure is currently 90%-95%.
  • A lack of trust of less-qualified peers.  Cardiologists often do not trust ED physicians to accurately diagnose a heart attack, resulting in a repetitious diagnosis process.
  • Most physicians are “…more loyal to their specialty than to the team with whom they work every day.”

Some of the solutions the Toyota production system offers include:

  • Decreasing wasted time can increase quality.  ”In 2002, for instance, our morality rate for coronary bypass surgery was nearly 4 percent.  After several kaizen projects in this area, typically removing 40 percent of the waste each time, mortality dropped to 1.4 percent in 2008 and has been 0 percent through six months of 2009.”
  • Making medical care more collaborative can improve care. For instance, in one hospital’s Collaborative Care wing, the nurse owns the care process. “The nurse remains in contact with the doctor but does not wait for instruction. Often, it is the nurse who instructs the physicians about a needed step or a critical time in the patient’s care.”

This quality improvements are sound good on paper, but take serious efforts to implement in practice.  In addition, current insurance payment schemes are not conducive to collaborative care.  Touissaint claims that Medicare pays $2,000 less per patient on average in Collaborative Care than in a traditional medical wing.

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Did pay-for-performance work in the UK?

February 15, 2010 in International Health Care Systems, P4P, Quality | No comments

A paper by Sutton, Elder Guthrie and Watt (2010) describes the UK’s National Health Service’s (NHS) adoption of the Quality and Outcomes Framework (QOF) in April 2004. In general, P4P programs can have positive or negative spillovers.  An example of a positive spillover would be the adoption of EMR to comply with certain P4P initiatives, but which also improves productivity in other areas.  A negative spillover would occur if physicians focus on getting the P4P bonuses, but decrease effort in unmeasured areas which could be more important to the patient’s health.

The authors describe the QOF more fully as follows:

In the first 2 years of the QOF scheme, practices were rewarded according to the performance they reported on 146 indicators. Through their performance on these indicators, practices earned up to 1050 QOF points. Practices were rewarded for these points according to a complex, non-linear function of the prevalence of disease in, and size of, their registered populations…An average practice was paid £75 per point in the first year and £125 per point in the second year of the scheme.

Seventy-six of the indicators, and 57% of the financial rewards, were offered for the quality of clinical care. These 76 indicators were focused on the care of people with 10 targeted chronic diseases and involved the maintenance of disease registers, the verification of diagnoses, the recording and management of risk factors and the provision of selected treatments. Points were awarded based on reported coverage rates if they were above a lower threshold of 25%. Maximum points were awarded if the coverage rate equalled or exceeded an upper threshold, which varied across indicators.

The authors examine whether the QOF increased effort levels for physician treatment of five groups of patients who had diseases for which physicians could earn QOF bonuses.  The paper also examines changes in quality for “Untargeted” patients, who did not have any diseases for which physicians could earn additional income under QOF.  The analysis is based on a before and after structure.  It evaluates the change in both quality metrics that were reimbursed under QOF and the quality metrics which were not subject to QOF bonuses.  If there are negative spillovers, one would expect the rewarded metrics would increase after QOF, but the unrewarded quality would decrease as physicians shift their priorities.

The paper finds that the effect on incentivized factors was substantially larger on the targeted patient groups (+19.9 percentage points) than on the untargeted groups (+5.3 percentage points).  The authors claim that there was no obvious evidence of effort diversion but there was evidence of substantial positive spillovers (+10.9 percentage points) onto non-incentivized factors for the targeted groups.

One issue here is that paper only looks at observable quality metrics.  The physician may believe that observable quality that is not currently rewarded will be rewarded in the future.  Thus, these positive spillovers may extend over to quality factors observable by the NHS.  On the other hand, unobserved quality may have decreased, but this would not have shown up in the econometric analysis.

  • Sutton, Elder Guthrie and Watt (2010) “Record rewards: the effects of targeted quality incentives on the recording of risk factors by primary care providers,” Health Economics, v19(1):1-13.

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Does a Hospital’s Ranking Increase Patient Volume?

February 1, 2010 in Quality | 2 comments

The US News and World Report has popular rankings for colleges, graduate schools and now hospitals.  Do patients actually decide to seek care at different hospitals based on these ratings?  Devin Pope attempts to answer this question in a recent Journal of Health Economics article (free draft).

To determine whether or not this is the case, Pope uses two methods.  First, he employs a fixed effects framework.  This method in essence evaluates how a change in the hospital rank affects the change in the volume of non-emergency patients.  The second method uses a mixed-logit discrete choice model (a.k.a. the random coefficient logit model).  This is based on a random utility framework and is different from the standard conditional-logit model; most importantly, it does not exhibit the logit’s restrictive independence of irrelevant alternatives (IIA) property.

To determine the change in patient volume, the paper focuses on Medicare patients in California.  The reason for this is because Medicare patients have flexible coverage and each hospital receives the same payment from Medicare from each patient.

The paper finds that a hospital-specialty that improves their within state rank by one spot experiences a 6.8% increase in patient volume on average.  The effect is largest for heart problems.  This may imply that patients are most responsive to changes in rankings for heart treatments.

Pope also conducts a number of robustness checks.  One of these is to examine how changes in a hospital’s rankings affect the volume of emergency care.  Since emergency care patients likely have little choice over the hospital to which they are admitted, a change in rankings should have little impact on patient volumes.  This turns out to be the case.

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What are Accountable Care Organizations?

January 26, 2010 in Medicaid/Medicare, Physician Compensation, Supply of Medical Services | 12 comments

Accountable Care Organizations (ACOs) are the latest rage in the health policy world.  The question is, what are ACOs.  The Urban Institute’s Kelly Devers and Robert Berenson try to answer the following question: “Can Accountable Care Organizations Improve the Value of Health Care by Solving the Cost and Quality Quandaries?”

The goal of ACOs is to pay providers in a way that encourages them to work together, to pay providers in a way that does not encourage supplier induced demand, and to create an organization that is rewarded for providing high quality care.  What kind of organizations are currently poised to evolve into ACOs. This chart evaluates the prospects.

One question is why doesn’t Medicare just use their current Medicare Advantage program to accomplish these goals.  In the Medicare  Advantage program, Medicare pays a lump sum to private insurers and holds them accountable for all the medical care the beneficiary needs.  However, there are three main differences between ACOs and HMOs.

  1. The “accountability” rests with the providers.  Providers or provider groups, rather than insurance companies, are evaluated on the quality and efficiency of care.
  2. Direct contracting with provider organizations without the reliance on a health plan intermediary.
  3. The ACOs allow for flexibility in the type of organization.  Some regions may prefer independent practice associations (IPAs) while others  may prefer a physician-hospital organization (PHO).

The physician-centered organization makes much sense to many policymakers because “the resources that flow from the decisions physicians make with patients account for a major portion of overall health care costs, regardless of where the care actually takes place.”

Medicare could pay ACOs with a “gainsharing” mechanism.  In the gainsharing framework, the fee-for-service payment structure remains, but a portion of patient cost savings gets passed through to the physician. On the other hand, Medicare could institute a partial capitation scheme.  This would be similar to Medicare Part D, where the prescription drug plans get a flat rate per person, but they also receive are involved in risk corridors, which “limit a prescription drug plan’s potential losses should the plan happen to experience much higher utilization and costs than expected.”

One problem with this framework is that physicians are good at treating patients, not at risk management.  Thus, many physicians may get stuck with high-risk patients and some ACOs may become insolvent unless there are adequate Medicare risk adjustment payments.

Secondly, patients may see ACOs as HMOs in disguise.  ”[I]f beneficiaries believe that ACOs are essentially tightly managed ‘HMOs in drag’ that are going to restrict their choices, undermine the doctor-patient relationship, and result in cheaper but lower-quality care, the concept will be met with skepticism, if not overt opposition.”

Other obstacles to ACOs include possible FTC and DOJ desires to quash ACOs on anti-trust grounds.  Further, state governments may need to change laws related to insurance regulation as well as organizational and professional liability.

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