Using publicly available hospital data, I developed a map to help you easily identify and understand how your hospital ranks with respect to two key outcome measures: 1) Mortality rates and 2) Re-admission rates. These outcome measures were used to calculate new outcome metrics (Survival Rate and Non-Readmission Rate) where higher scores reflected better performance (scores can range from 0 to 100). Details of how these metrics were calculated appear below the map.
In the map below , the hospitals can have one of four colors. For Mortality (Survival Rate), the colors for each hospital are based on their relative performance compared to the US average across three mortality rate measures (heart attack, heart failure, pneumonia). Hospitals were coded as follows:
- Red = Worse
- Yellow = No different than US average
- Green = Better
Hospitals that do no have data are coded as Blue.
As you can see in each map, there are plenty of hospitals coded with a yellow button, indicating that, of the hospitals that have health outcome data, you can not distinguish among most of them when it comes to mortality and re-admission rates. There are a handful of hospitals that are either better or worse than the US average, but most of them are roughly the same.
If you click on one of the buttons, you will see detailed information about the survival/non-readmission rates for each hospital.
- The following map is for mortality rates (Survival Rates)
Map of US Hospitals and Patient Survival Rate
Map of US Hospitals and Patient Non-Readmission Rate
Measures that tell what happened after patients with certain conditions received hospital care are called “Outcome Measures.” We use two general types of outcome measures: 1) 30-day Mortality Rate and 2) 30-day Readmission Rate. The 30-day risk-standardized mortality and 30-day risk-standardized readmission measures for heart attack, heart failure, and pneumonia are produced from Medicare claims and enrollment data using sophisticated statistical modeling techniques that adjust for patient-level risk factors and account for the clustering of patients within hospitals.
The death rates focus on whether patients died within 30 days of their hospitalization. The rates of readmission focus on whether patients were hospitalized again within 30 days. Rates of readmission show whether a hospital is doing its best to prevent complications, teach patients at discharge, and ensure patients make a smooth transition to their home or another setting such as a nursing home.
Three mortality rate measures were included in the healthcare dataset for each hospital. These were:
- 30-Day Mortality Rate from Heart Attack
- 30-Day Mortality Rate from Heart Failure
- 30-Day Mortality Rate from Pneumonia
Mortality rate is measured in units of 1000 patients. So, if a hospital has a heart attack mortality rate of 15, that means that for every 1000 heart attack patients, 15 of them die.
The party responsible for collecting the data included another piece of important information for each of these mortality rate measures. The data set included information for each hospital that indicated whether the hospital’s mortality rate was worse, better or no different than the US average.
Three readmission rate measures were included in the healthcare dataset for each hospital. These were:
- 30-Day Readmission Rate from Heart Attack
- 30-Day Readmissions Rate from Heart Failure
- 30-Day Readmission Rate from Pneumonia
Readmission rate is measured in units of 1000 patients. So, if a hospital has a heart attack readmission rate of 15, that means that for every 1000 heart attack patients, 15 of them were readmitted.
The party responsible for collecting the data included another piece of important information for each of these readmission rate measures. The data set included information for each hospital that classified the hospitals on whether their readmission rate was worse, better or no different than the US average.
Calculating the Outcome Metrics
This classification metric (e.g., better, same, worse) was used to help color-code the different hospitals for mapping purpose. Better was coded as green. Same was coded as yellow. Worse was coded as red. Hospitals that did not have data for that metric were coded as blue.
Mortality Rates and Readmission Rates are scores so higher scores mean worse hospital performance. To rescale the values to a 0 to 100 scale, where higher score s indicate better hospital performance, I simply rescaled each hospital’s mortality/readmission rates (out of 1000) into a new metric (0 to 100) using the following formula (Hospitals that had zero death rate would receive a Survival Rate Metric score of 100. Higher death rates lead to lower Survival Rate scores.).
New metric = Original metric * (1 – (Rate / 1000)) * 100
Survival Rate Metrics were calculated as:
Survival Rate Metric = Mortality Rate * (1 – (Rate / 1000)) * 100
Non-Readmission Metrics were calculated as:
Non-Readmission Metric = Readmission Rate * (1 – (Rate / 1000)) * 100
This transformation does not result in any loss of information. The rescaled values (Survival and Non-Readmission Rates) are correlated perfectly (r = 1.0) with their original counterpart (Mortality and Readmission Rates).
Ranking Hospitals by Health Outcome Color Codes
I created two Hospital maps for each category of outcomes (Mortality and Readmission). I used the classification variable when rates were worse, better or no different than the US averages. Mortality/readmission rates that were classified as “Number of cases too small, “ “No data are available from the hospital for this measure” or were blank were not used to map the hospitals. These hospitals appear as blue on the map. Using the three responses (worse, better or no different), an overall score (Survival Rank) was calculated by averaging over the three mortality rate metrics where:
- Worse coded as 1
- No different coded as 2
- Better coded as 3.
This Survival Rank scores were used to bucket hospitals into one of three levels:
- Red: 1 thru 1.5
- Yellow: greater than 1.5 thru 2.5
- Green: greater than 2.5