Asp reporting web

We were growing more inefficient each week. My colleagues at Health Connecticut were convinced there was a better way to manage the data we collected from and provided to our 22 member hospitals. We were using Access and SQL queries, a slow process because it tapped an entire database of information without providing us with specific data we needed.

One of the missions of Health Connecticut, a consortium affiliated with the Connecticut Hospital Association but owned by the hospital members, is to provide hospitals with information services to support them in their managed care contracting and population health management activities.

By bringing together claims data from multiple payors in the state, we hoped to create a comprehensive warehouse of information that would provide the quality assurance staff and financial analysts at all 22 owner hospitals with a powerful analytic repository to support their demand planning, disease management program design and contracting activities.

But we had to reinvent the wheel every time we ran a query or issued a report. For example, isolating pharmacy data for pneumonia cases could take up to six weeks. Some queries to our data warehouse could take several days to complete, depending on complexity. We use typical healthcare measures across our analysis, which were being recalculated for each dataset. This excessive use of staff time convinced us it was time to make a bold change: We needed a Web-based system everybody could use.

Finding a Solution

Health Connecticut established a subcommittee with representatives from each of the hospitals to find a commercial solution. The process was long--almost 18 months--and encompassed two RFPs and examining nearly a dozen vendors' products.

In January 1999, we issued our first RFP and subsequently conducted multiple rounds of interviews, but had difficulty identifying even one vendor and decision support product with the Web-based reporting functionality we sought. One year later, we issued a second RFP. This time, we concluded the process in less than four months with our selection of W3Health Corp., a company that delivers analytic software solutions to healthcare payor and provider organizations through an application service provider (ASP) model and features a distributed reporting system.

We selected W3Health for several reasons. Their price was competitive, and they guaranteed us they could meet our implementation timeline. We had spent nearly a year on the first RFP, so we wanted to be operational as soon as possible. We also wanted a vendor that would incorporate our business rules into their product rather than the reverse.

Implementation began in mid2000 and was completed by the fall. During this time, W3Health processed 6 million claims and created a viable test product in just four weeks from claims, eligibility and provider data.

Easy Implementation

With the testing complete and the functionality claims proven, we moved into the customized implementation phase, in which the product was tailored to fit our requirements, needs and rules.

For our initial implementation, we created a task force of 10 members from both internal Health Connecticut users and external users (either managed care or utilization management from our member hospitals). The core user group received two full days of on-site training and then went into full user mode.

Next, Health Connecticut expanded the user group, training 30 people from the 22 member hospitals. We had a users group meeting that was well attended, but we have not measured the actual use they are making of the system independent from the reporting we have done.

In the wake of terrorist attacks last September, users are increasingly focusing on bioterrorism preparedness as a key issue. At our next users group we will attempt to poll the group and develop collective ideas for future studies.

Also, Health Connecticut is currently working on measuring antibiotic utilization for acute otitis media and will use that study as a springboard to examine resistant organisms and antibiotic usage across other disease states. It will be particularly interesting to review the spike in antibiotic usage following anthrax attacks. Since an elderly Connecticut woman died due to anthrax exposure, we are likely to see an increase in antibiotic prescriptions.

It will take about a year before we see that reflected in our claims data, allowing for runoff and completion factors. But the entire scope of that study hasn't been defined yet. Unfortunately, the anthrax attacks are going to have a negative effect on the work that had been done to try to reduce the demand by patients for antibiotics suffering from common colds.

Technology at Work

The W3Health DRS system uses online analytical processing (OLAP) technology to essentially predigest data into cubes or related data. Hierarchical categories, called dimensions, and healthcare measures in standard or custom form, are built into the system based on our business rules. Users designate the healthcare measures they want to report on and select the dimensions the report should contain. Detailed data is rolled up to summary levels and can be drilled down to fine detail, all with a few clicks of a mouse. Users need not know how to write queries or program code.

The product incorporates Symmetry's Episode Profiler[R], a tool that allows us to examine treatment costs and utilization for a particular disease across the entire continuum of care. Previously, there was no way to link inpatient claims to outpatient claims to pharmacy data for any particular disease state.

For instance, if we had a claim for a heart attack, we could review the inpatient costs associated with treating the patient, but couldn't look at all of the subsequent follow-ups in the physician's office or view the drugs that were prescribed specifically to treat the patient. We also wouldn't know if the patient received homecare services following the heart attack or if there were special equipment costs.

Episode Treatment Groups allow us to accurately group all those costs and analyze them in concert, a feat we were previously unable to accomplish no matter how many queries we ran.

Beneficial Returns

From a cost standpoint, Health Connecticut was well served. We paid an implementation fee and now pay a monthly service fee, but W3 has absorbed the hardware and storage costs.

Health Connecticut anticipates an average yearly savings of nearly $300,000 in salaries and equipment by using an ASP platform and Web-technology instead of pursuing an original, homegrown solution, since we do not have to invest in an IT infrastructure for the results we achieve.

To run a homegrown solution, we would have had to employ seven people, buy separate data and Web servers and obtain our Oracle license. Using an ASP model, we employ two full-time workers, a research analyst and an administrative assistant. Our ongoing cost is the annual license fee, but we do not dedicate any IT staff to run the application.

We hope to demonstrate opportunities for even greater savings related to disease studies. We have taken an evidence-based medicine approach and applied that to data to develop a best practice analysis among the 22 hospitals.

Savings per Disease State

Under our community acquired pneumonia study, we project $8 million in savings potential over two years for hospitals treating Medicare pneumonia patients. At first glance, the $8 million savings may appear modest because it is spread across 22 hospitals and two years, but that represents just one disease state. Once we factor in the myriad of other high-cost or high-volume conditions, opportunities abound for the hospitals to reduce the cost of providing that care without sacrificing quality.

In just over a year, we have gained the ability to look at the entire patient population and quickly identify health trends and the potential costs of those trends. Analysis that used to take weeks or months to create now takes a fraction of that time. Because of OLAP technology, users can obtain pharmacy data, for example, and isolate a particular subset of data in a few clicks instead of a few weeks.

The decision-support application allows us to fulfill our obligation as administrators to a contract that covers about 100,000 lives, and we can complete analyses with severity ratings that are acceptable for most physicians.

System users can perform custom analyses at their own desks rather than waiting for a hard copy to be delivered to their offices. They can modify the parameters of their reports on the fly because no programming or querying knowledge is required. We are also pleased that the product offers current functionality that extends well beyond the limitations of a legacy system.