Quality

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The answer is probably not.  The NCQA defines 149 factors which would make a practice a successful medical home.  These include physician access during and after office hours, electronic access to patients information, availability of clinical data and use of that data for population management, identification of high risk patients, ability to refer patients to available community resources, care coordinate, and quality measure tracking.

As recent Health Economics articles finds that almost half of physician practices fail to meet the NCQA’s medical home standards.  Specifically,

Forty-six percent…of all practices lack sufficient medical home infrastructure. While 72.3 percent…of multi-specialty groups would achieve recognition, only 49.8 percent…of solo/partnership practices meet NCQA standards. Although better prepared than specialists, 40 percent of primary care practices would not qualify as a medical home under present criteria.

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The California Health Care Foundation (CHCF)’s Health Care Almanac provides some unique insights on trends in health care quality in California and for the United States as a whole.  Many of the national figures for the Almanac come from the CDC (BRFSS and Vital Stats) and AHRQ’s National Healthcare Quality Report.  California quality figures come from the California Department of Public Health, the Office of Statewide Health Planning and Development and the California Health Interview Survey.

Although not discussed in this post, another portion of the Health Care Almanac looks at quality by site of service.  Much of this data comes from Hospital Compare, CMS OASIS data, AHRQ’s National Healthcare Quality Report, and the Dartmouth Atlas.

Today I highlight 3 topics related to clinical quality:

  • Cesarean Deliveries
  • Infant Mortality
  • Cancer Incidence.

More detail is below.

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Medicare’s push to evaluate all types of providers is being extended to Inpatient Rehabilitation Hospitals (IRFs).  According to Health Reform bill (specifically, Section 3004 of the Affordable Care Act), CMS is required to start publishing quality measures for IRFs by October 1, 2012. This newly created IRF Quality Reporting Program (QRP) currently has proposed two measures.  These include the following:

  • Presentation of Urinary Catheter-Associated Urinary Tract Infections (CAUTI)
  • Presentation of Percent of Residents with Pressure Ulcers that Are New or Have Worsened

CMS will hold an Open Door Forum on Tuesday, November 29, 2011, 2pm-4pm ET to discuss these measures.  It is disappointing that CMS only has two quality measures for the IRF program.  Thus, the QRP is far less comprehensive then Health Reform intendend.  Hopefully, the number of quality measures increases over time.  The Healthcare Economist does realize, however, that rehabilitation services are much harder to evaluate than more procedure based services with more observable outcomes.  Specifically, improvement in patient functioning is a key measure for IRFs.  However, if the IRFs themselves self-report this data, the quality measures will not be unbiased.  I am assuming that the data for the IRF QRP come from the IRF Patient Assessment Instrument (PAI), and thus the quality data will be self-reported by the IRFs themselves.  Here is the form used by IRFs as part of the PAI.

One problem with any quality system is cases where the provider fails to report the quality data.  In the QRP, however, IRFs will have a strong financial incentive to report these measures.  Specifically, if an IRF fails to report their quality measures, Medicare will reduce their payments by 2 percentage points.

 

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Starting in fiscal year 2014, Medicare will start rewarding hospitals with high quality care and penalizing hospitals with low quality care.  The rewards and penalties will be financial in nature. High-quality hospitals will receive a bonus and low-quality hospitals will receive a financial penalty.  There is a lot of existing documentation on this hospital value-based purchasing (HVBP) program such as:

One component of the HVBP is patient satisfaction.  Some policy experts believe that patient satisfaction is of the utmost importance.  If Medicare evaluates hospitals based on patient satisfaction, then hospitals will compete to improve how well patients are satisfied. A New York Times article already mentions some of the efforts hospitals are undertaking to improve patient satisfaction.  For example,

  • Improving the quality of food
  • Renovating units
  • Creating more single units (compared to shared units)
  • Having nurses visit rooms hourly
  • Creating scripts for doctor-patient and nurse-patient interactions
  • Quicker response time ["Jefferson Regional Medical Center in Pittsburgh expects all employees, from maintenance workers to doctors, to respond to a patient’s call light or find someone to offer assistance."]
  • Building more elevators.

Elevators!?!?!  It turns out that “NYU found that long waits at its elevators drove down its scores, so now it is building a new bank of elevators.”

Hospitals complain, however, that they may only have a limited ability to influence ratings.  This is certainly true in some cases. For instance, patient expectations of the standard of care they receive may vary regionally.  For example,

…some of the nation’s most prestigious hospitals, including Cedars-Sinai Medical Center in Los Angeles and the University of Chicago Medical Center, get lower marks from patients on most areas of patient experiences, according to the government’s Hospital Compare Web site.

So do many of New York City’s elite institutions…Some hospitals, like NYU, get bad patient reviews even as they score average or superior in measures of clinical care from the government and accreditation groups.

‘People in New York have very high expectations about what it means to be taken care of,’ said Dr. Katherine Hochman, an NYU physician. ‘When they don’t get their food on time and have to spend eight hours in the emergency department, well, that’s just not their image of what a world-class institution is.’

Further, many providers believe that indigent patients give physicians lower quality scores even though these patients receive the same care as do richer patients.  Hospitals with more Medicaid-eligible patients could receive lower patient satisfaction scores due to case mix alone rather than due to actual quality.

To account for these confounding factors, Medicare can institute a risk adjustment mechanism.  By including patient income (or Medicaid) status in their model, however, Medicare would implicitly be allowing hospitals to provide a lower standard of care to the poor. Alternatively, if the poor do in fact give lower satisfaction scores, than hospitals may have an incentive to avoid these patients.

Similarly, including regional indicators in the risk adjustment model can also be problematic.  If New Yorkers have higher standards than individuals from Iowa, then one may want to normalize performance regional.  If CMS adopts this specifications, hospitals in essence would only be compared against their local peers.  Areas which have consistently below average care–in terms of patient satisfaction–may not be punished if they are the ‘best of the worst’ in their area.

Although patient satisfaction is not always correlated with high quality medical care, paying hospitals more for care that meets their patients’ needs does seem to be a sensible solution.

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Medicare beneficiaries have a choice: pick the standard Medicare fee-for-service (FFS) benefit or rely on managed care plans to supply their healthcare through the Medicare Advantage (MA) program.  Many Medicare beneficiaries prefer MA because it offers them lower out-of-pocket costs and provide benefits not available in the traditional FFS Medicare program. Other beneficiaries prefer the FFS benefit because MA plans typically restrict provider choice in an effort to control costs.

The quality of care in Medicare MA relative to FFS, however, has still not yet been consistently evaluated.  Because beneficiaries can switch from MA to FFS each year, if quality is low, healthy individuals may prefer MA to reap the reduced cost sharing benefits, but when they become sick they may switch to Medicare FFS.

A study by Elkin and co-authors evaluates whether or not this is the case for beneficiaries who get cancer.

Data and Methodology

We identified Medicare managed care enrollees aged 65 years or older who were diagnosed with a first primary breast (n = 28 331), colorectal (n = 26 494), prostate (n = 29 046), or lung (n = 31 243) cancer from January 1, 1995, through December 31, 2002, in Surveillance, Epidemiology, and End Results (SEER) cancer registry records linked with Medicare enrollment files. Cancer patients were pair-matched to cancer-free enrollees by age, sex, race, and geographic location. We estimated rates of voluntary disenrollment to fee-for-service Medicare in the 2 years after each cancer patient ’ s diagnosis, adjusted for plan characteristics and Medicare managed care penetration, by use of Cox proportional hazards regression.

Results

The authors find that MA beneficiaries with cancer are less likely to switch to FFS than a cancer-free beneficiary. The hazard ratios range from 0.78 for colorectal cancer to 0.86 for prostate cancer. The results were consistent across various age, sex, race, cancer stage and region strata.

The likely reason for this finding is that people who have a serious disease do not want to change coverage. Even if the FFS benefit offers improved access to better care, there are significant costs of switching coverage. The new FFS providers may have less knowledge of the individual beneficiary’s health condition and the change can be stressful for the beneficiary as well. A worthwhile analysis to confirm whether this is the case would be to examine whether FFS beneficiaries who contract cancer are more likely to switch to a MA plan after contracting cancer. If the transaction cost/care coordination is driving Elkin’s results, then FFS beneficiaries with cancer should also be less likely to switch to MA than cancer-free FFS beneficiaries.

It could also be the case that MA provides high quality care for the most prevalent cancers (i.e., prostate, lung, colorectal, and breast), but there is a significant improvement in quality when beneficiaries visit FFS providers when they have rarer diseases. To confirm whether or not this is the case, the authors examine whether beneficiaries with non-Hodgkin lymphoma, acute leukemia, and soft tissue sarcoma are more likely to switch to FFS. The authors found no effect of these cancer diagnoses on the likelihood of disenrollment from a managed care plan.

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For nursing homes at least, patients do not seem to have much choice.  According to an article by Grabowski and Town:

The introduction of the NHQI was generally unrelated to facility quality and consumer demand. However, nursing homes facing greater competition improved their quality more than facilities in less competitive markets…The lack of competition in many nursing home markets may help to explain why the NHQI report card effort had a minimal effect on nursing home quality. With the introduction of market-based reforms such as report cards, this result suggests policy makers must also consider market structure in efforts to improve nursing home performance.

In general, there are many reasons why patients do not respond to provider report cards.  It could be the case that the provider is a monopoly, and thus the patient has little choice of providers.  Alternatively, patients may not be aware of the quality metrics.  One would thing that high quality providers, however, would spend money advertising their high quality ratings to make patients aware of their services.  In other cases, the patients may not be the ones directing care choices.  Providers may be the ones who are the de facto selectors of care.

Patients could also not believe that the CMS quality ratings are very useful.  They may prefer other sources of information on medical quality such as their friends, relatives, or physicians.  Thus, it may be the physician who actually chooses to which nursing home the patient will go.  If the physician has incentives to sent the beneficiary to nursing homes in the network or simply does not wish to spend the time analyzing nursing home quality, then patients may be less likely to be allocated to high quality nursing home.

 

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The Agency for Healthcare Research and Quality’s (AHRQ)  Healthcare Cost and Utilization Project (HCUP) is a family of databases and tools intended to improve the quality, safety, efficiency, and effectiveness of the U.S.   health care system.  HCUP results from Federal-State-Industry partnership to build a comprehensive all payer data system.  A summary of the databases available from HCUP can be found here.  A summary is also provided in the table below.

Abbreviation

File Description States Participating

Available From:

SID State Inpatient Databases Data on 95 percent of community hospital discharges 44 1990
SASD State Ambulatory Surgery Databases Ambulatory surgical center data (hospital and free-standing) 29 1997
SEDD State Emergency Department Databases ED visits that do not result in hospitalization 29 1999
NIS Nationwide Inpatient Sample All-payer inpatient care database. A 20% stratified sample of U.S. community hospitals. varies by year 1988
NEDS National Emergency Department Sample All-payer inpatient ED database. A 20% stratified sample of U.S. community hospitals. 2006
KID Kids’ Inpatient Database All-payer inpatient database for children.  Contains 3 million discharges form 3,500 community hospitals. 1997

 

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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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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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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.

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