Why did you decide to focus on hospitals?
Hospital services are the single largest component of health care spending, and the decisions made by doctors and hospitals about who gets admitted, how long patients stay in the hospital, whether they go to the ICU, etc. drive both care and cost. A hospital’s affiliated physicians decide who is admitted as well as the amount and type of care those patients receive. In turn, the probability of being hospitalized or admitted to the ICU is related to the capacity of the hospital compared to the size of the population it serves. The more hospital beds there are per capita, the greater the likelihood the patients will be admitted.
How were hospitals selected?
We report data for acute care general hospitals, or those that provide a range of acute care services to Medicare fee-for-service patients. Hospitals were also selected by size, as determined by the number of persons “assigned” to each hospital (by linking Medicare claims by each enrollee to the hospital he or she used during the study period). The study was confined to hospitals with large enough populations to result in statistical stability and retain the confidentiality of patient information. Inpatient data for hospitals with at least 80 deaths during the study period are provided on the Web site; for Part B data, which is based on a 20% sample of deaths, a hospital had to have at least 400 total deaths (80 deaths in a 20% sample) during the study period to be included.
Why do you focus on patients who were chronically ill and in their last two years of life?
One reason is the growing concern about the way chronic illness is managed in the United States, and about the possibility that some chronically ill and dying Americans might be receiving too much care: more than they and their families actually want or benefit from. Our emphasis on this period of life is also motivated by our interest in developing measures of efficiency and performance that minimize the chance that variation in the care delivered in different regions and by different hospitals can be explained by differences in the severity of patients’ illnesses. By looking at care delivered to patients with similar illnesses during fixed intervals of time prior to death, we can say with assurance that the prognosis of all the patients in the cohort is identical – all were dead after the interval of observation. By further adjusting for difference in age, sex, race, and primary chronic illness, we believe that we have developed fair measures of the relative intensity of care provided to equally ill patients – comparisons for which differences among patients are an unlikely explanation
How do you ensure some patients were not more severely ill than others?
The study only focused on patients who died so we could be sure that patients were similarly ill across hospitals. By definition, the prognosis of all the patients in the cohort was identical – all were dead after the interval of observation. Therefore, variations cannot be explained by differences in the severity of individuals’ illnesses.
What are the medical conditions that define a patient as having a chronic illness?
To be assigned to our chronically ill cohort, a patient must have one of the following nine conditions: congestive heart failure, chronic lung disease, cancer, coronary artery disease, renal failure, peripheral vascular disease, diabetes, chronic liver disease or dementia. ICD-9-CM codes defining each condition can be found here.
Why do the national average population and rates given in the state and regional tables not match the numbers given in the hospital tables?
In the state and regional studies, the study population was a 20% sample of resident enrollees with one or more of the nine chronic illnesses, regardless of whether they were hospitalized during the last two years of life. In the hospital-specific studies, only decedents who had one or more medical hospitalizations for one of the nine chronic illnesses were included. Medicare enrollees who were hospitalized with one or more of the nine chronic illnesses were assigned to the hospital most frequently used during the last two years of life. A 100% sample of deaths was used for the inpatient utilization rates; a 20% sample was used for Part B rates.
The rates given for inpatient sector spending do not match the rates given for inpatient facility reimbursements. What is the difference between these two measures?
Sector spending includes Part B (physician) spending that occurred at each site of care; Part B payments for physician services delivered in each type of facility (acute care hospital, skilled nursing facility, hospice, etc.) were added to the facility payments to determine overall spending in the sector. The facility reimbursement rates do not include Part B spending for physician services.
What do you mean by efficiency?
Our approach to evaluating relative efficiency is based upon the notion of benchmarking, which entails a comparison across hospitals (or regions) along the dimensions of both quality and resource use. For example, within a given market area, one could identify the most efficient hospital based upon its relative performance in terms of quality (equal or better to all others) and costs (using fewer resources than others). It is possible, however, that the most efficient hospital within a given market would still be less efficient than “benchmark” hospitals identified in other regions.
If a hospital is seen as inefficient, does this mean that it provides poor care?
Our studies do not directly measure the quality of care. Instead, they focus on what could be called overcare – hospitalizations and procedures that cost money but do not provide a corresponding benefit. (Large numbers of days in intensive care during the last six months of life, for example, neither extend life expectancy nor provide high quality of life for the patient.) Care is often described as “poor” if the process of care is poor; this study looks not at whether the thing was done right, but if whether the decision to provide the hospitalization or procedure was the correct decision to begin with.
Where can I find direct measures of hospital quality?
The quality of care can be evaluated using accepted technical process measures such as those that can now be found on the CMS’ Hospital Compare web site. We provide summary scores on five measures for treatment of heart attacks (AMI); two for congestive heart failure (CHF); and three for pneumonia, using methods developed by Jha et al. In addition, we report a composite score, which is the weighted average of the three condition-specific summary scores. These measures are available for hospitals that had at least 25 patients in the sample for each measure, as well as for HRRs and states (weighted averages of the scores for hospitals located in each region or state).
Will patients pay more out-of-pocket expenses with an inefficient hospital?
Patients and their families who choose hospitals that tend to deliver more intense care may have to pay for that extra care out of pocket. Medicare sets the overall price for physician services and pays 80% of that amount directly to the physician, leaving patients responsible for the remaining 20%, which they must pay unless they have supplemental insurance or are covered by Medicaid. Medicare also requires a 20% co-payment for durable medical equipment, such as wheelchairs and oxygen for home use. Therefore, the patient’s share of the cost of care can vary considerably depending upon which hospital is chosen.
How do we know that patients at some “outlier” hospitals are not really sicker (i.e. do they have more co-morbidities)? And if we say no, how do we prove that?
The Dartmouth Atlas uses standard statistical adjustment methods to adjust for differences in age, sex, race and the relative predominance of the nine severe chronic conditions among the populations of the hospitals we study. Even after these statistical adjustments are made, some hospitals have substantially different rates than would be expected given the level of illness and the age, sex and race composition of their populations, indicating that it is not sickness, but practice style (propensity to use more specialists and to treat patients inside the hospital) that results in such rates.
Your determination of the intensity of terminal care includes the% of patients who died during a hospitalization that included an admission to intensive care. You cite variation, yet why is this significant? Was there a higher or lower death rate associated with ICU admission?
This measure attempts to capture the relative aggressiveness of care at the end of life. Admission to intensive care is an extremely aggressive intervention that has no measurable value to a dying person. In light of the evidence that more aggressive care in managing patient populations with chronic illness does not lead to longer length of life or improved quality of life, higher scores on this measure can be viewed as an indicator of lower quality of death, and subjects that person to pain and suffering that do not extend life but diminish the quality of life.
If payers utilize this data, as your study suggests, and direct their chronic disease populations to low-cost and low-utilization hospitals, aren’t you limiting a patient’s life-saving options?
Quite the contrary. The evidence is that higher utilization does not extend life expectancy, and might be correlated with shorter life expectancy, compared to lower utilization. Therefore, sending people with chronic diseases to higher-efficiency, lower-utilization hospitals for their care could result in both lower spending and increased quality and length of life.
Medicare restricts the revenue that a hospital can make on a specific diagnosis per hospital stay. How is it possible that some hospitals can have many more ICU admissions, and more specialist visits – are these not part of DRG guidelines? In other words, is increasing the volume of certain types of services a way in which providers can “game” the system? Are there other such loopholes that Dartmouth Atlas research exposes?
The issue is not one of explicit “loopholes”. It is that two factors are important in judging efficiency: volume (the number of discharges) and price (the payment per discharge). Medicare’s diagnosis-related group (DRG) system uses few guidelines or sets of rules governing when to admit, discharge or treat patients with specific, measurable conditions. The system actually encourages gaming – to maximize revenues through providing more acute care because it pays better than preventive or primary care.
Because DRGs reimburse hospitals on a per-case (per-discharge) basis, it is possible for some hospitals to have more cases (or more discharges) during a given period of time. This would increase total payments, and would most likely be due to the greater availability of beds relative to the size of the population compared to other hospitals. Physician services are paid on a purely fee-for-service basis, so more frequent visits would result in higher payments.
An oversupply of beds makes it easier to admit and readmit (what is known as “churning”) patients in both acute care and ICU beds. Admitting physicians have discretion about whether or not to admit patients with many common conditions such as congestive heart failure, chronic pulmonary disease or cancer. In low-resource, low-utilization areas, such patients are treated outside the hospital. In high-resource, high-utilization areas, they are admitted and receive treatment as inpatients. Admission to ICU is also discretionary, and depends on physicians’ opinions about necessity and the available supply of ICU beds.
An additional opportunity for hospitals to increase revenue is “up-coding” patients in order to increase DRG payments by claiming patients are outliers – that they have more co-morbidities and complications than average.
Where can I find more information?
Comprehensive information on our hospital-specific data and methods is available in the Appendix on Methods of our 2011 report, “Trends and Variation in End-of-Life Care for Medicare Beneficiaries with Severe Chronic Illness.”