Warning signs: Ford said there are several places to look if a reporter believes their city's emergency medical services are lagging. "One real danger sign is look at the EMS to see if it has dual response or paramedic equipped fire trucks," he said. If paramedics are not riding on fire trucks, there could be a problem. In Hillsborough County, firefighters who had no paramedic training would often arrive long before EMS. Ford also suggested talking to emergency room doctors. The American Heart Association often has a local board that monitors response times.
Continued from page one: Vital signs
short). You can decipher them with a $17 CD-ROM sold by the National Center for Health Statistics. Several medical publishers put out coding manuals that are good to have handy.
Using UB data for medical research is not without controversy. Pitfalls include inconsistency in coding between hospitals, especially when it comes to areas with fuzzy definitions like various complications of labor. Experts also question whether hospitals reliably code their mistakes. And experts point out that the ICD codes do not yet distinguish between ailments that patients came in to the hospital with and those they acquire during their stay (pneumonia and infections being prime examples). Despite all this, a growing number of health researchers are using UB data because it's the best and only database available.
New Jersey's public UB records identify the hospitals, but not doctors' or patients' identities. Date fields are also scrubbed. Some states reportedly withhold even the hospital names.
Next hurdle
If you get this far, the next hurdle is risk adjustment. Hospitals argue, and experts agree, that it's unfair to compare hospitals that have different kinds of patients. The place with a higher mortality rate may have older or sicker patients. To compare apples and apples, you have to adjust for the patient case mix.
We briefly considered trying risk adjustment in-house using SPSS's logistic regression, but quickly became convinced it would not be a wise move. (Logistic regression takes string variables and predicts relationships. In medical data, it will tell if there is a strong relationship between, say, diabetes and kidney failure.)
The major concern was credibility. We were about to launch the most ambitious review of hospitals in the state's history. We were going to tell 7.7 million residents which hospitals were great and which ones were killing too many patients. Our information had to be solid, and we had to present evidence that was acceptable to the medical and regulatory communities. No one would take us seriously unless we used a well-known vendor who did risk-adjustment for hospitals and insurance companies.
Fortunately, we were able to forge a relationship with Inforum, Inc., a Nashville, Tenn., company whose parent firm, MedStat, essentially invented the medical risk-adjustment methodology 15 years ago. Inforum was looking to break into the New Jersey market and we were looking for attack-proof data. Inforum provided us risk-adjusted data in three areas: cardiac bypass surgery, stroke treatment and pneumonia/influenza care. We got the adjusted death rates, adjusted lengths of stays, and complication rates in each area and compared it to hospitals in eight other states. This was the basis for the report cards we published. We went a step further and extracted insurance, demographic and geographic data from the UB tapes. This helped us show how minorities of all ages were less likely than their white counterparts to undergo bypass surgery or pneumonia treatment. We also found women with insurance were more likely to have C-sections than poor women insured by Medicaid. Rates varied too between cities and suburbs.
Words of caution
Don't trust anybody or anything. We found errors with the raw UB data (incorrect hospital ID numbers), and Inforum's information (they told us one facility did not note secondary diagnoses when it did). Send your results to your target hospitals before you run and ask them to provide any data that is inconsistent with yours.
Be prepared for a heavy barrage of misinformation, lies and damn lies from all fronts.
Hospital executives and trade groups will talk out of both sides of their mouths. In the same breath that they disparage your consultant's work, they will tell you they use the same data for their own internal reviews. Ironically, the hospital association - which laughed at an early inquiry from us about how to examine the discharge data - came in midway through the series and asked to work with us. Our response: Pucker up and kiss our.
The impact of the series was almost immediate. The state's health commissioner promised to issue his own bypass report card in 1997, moved to standardize stroke and pneumonia care, promised to beef up his hospital inspection staff, and crack down on a Newark bypass center that was violating its license.
Rick Linsk and Paul D'Ambrosio can be reached at (908) 922-6000
REPLACE ALL astName ; WITH left (name, at ("," name) To do the right part of the name, you need to know how to use the substr( ) function. Here are two examples: ? substr (name, 6) n, William Jefferson
? substr (name, 6, 5) n, Wi
You could sweat this one out, or just go back to the top of this article for the answer.
Richard Mullins can be reached at (573) 882-2127, or send e-mail to richard@nicar.org