Our Solution, Phase 1:
Modeling Present Operations

We provide client specific tools to evaluate the impact that present operational decisions have on cost and quality performance. We examine in detail the interactive effect of patient demand, clinical practice patterns, facility capacity and staffing on cost and quality. In Phase One, we model the interactive nature of management decisions.

1. Demand Forecasting

   An accurate demand forecast provides the foundation for the development of the overall model. For obstetrical services, we use historical data to develop single year age group specific fertility rates. The fertility rate information permits analysis of the impact population changes and aging will have on overall birth volume. We also forecast birth volume by zip code and provide both commitment and relevance indexes. The commitment index measures the percent of a hospital's or plan's births from each zip code while the relevance index measures the market share or penetration. The demand forecast helps determine birth volume trends and geographical shifts in the target population. The birth volume forecast can also be used to translate births into the volume of outpatient visits required. Because they are zip code specific, these forecasts are extremely useful in determining the number and location of practitioners required in a network. Finally, the birth volume information is valuable in designing regional marketing strategies.

2. Practice Pattern Analysis

   Clinical practice patterns translate the birth forecast into inpatient workload. We analyze historical provider practices and explore the cost and quality ramifications of resource intensive practice patterns. Detailed analysis is performed of the cesarean delivery rate, non-birth related obstetrical admission rate and several average length of stay parameters. Scheduled procedures such as inductions and scheduled cesarean deliveries are analyzed by day of week and time of day. Administrative preferences regarding admission and discharge by day of week and time of day are also analyzed. Decisions in these areas transform birth volume into inpatient work, which is network or facility specific. Our model helps the administrative and medical staffs understand the specific resource implications of practice decisions. Instead of generalities, we link practice patterns to facility square footage, equipment, and support staff requirements. We enhance the client's ability to negotiate with a medical staff and/or help establish regional clinical targets.

3. Regional and Facility Sizing

   Based on the results of practice pattern analyses, we use operations research techniques to model the number of facilities needed in a region and the size of each facility. When either expanding or realigning capacity, construction costs of an obstetrical bed average around $300,000. Therefore, the sizing decisions can have important cost ramifications. Rather than a "black box," the sizing models are built interactively with clinical and administrative staff involvement. Many times the interactive process provides as much value as the final model, in large part by helping to build consensus. The interactive nature of the modeling process gives the medical, marketing, fiscal, and administrative staff a common way of exploring the options available and understanding the impact on the facility or network. Using the same tools, we help the client determine the number of beds needed by each facility, assuming the clinical targets are achieved. In addition, the client gains significant insight into how regional capacity variations translate into cost differences.

4. Staff Scheduling

   Using optimization techniques, we produce weekly rotating nurse work schedules. Our approach has an advantage over more traditional methods of scheduling staff because, rather than using average staff requirements by shift, our schedules are based on hourly labor requirements. While the time a specific patient will arrive at a facility is largely a random process, we can predict the number of patients who will arrive within a given time period quite accurately. Using historic institutional data on how patients proceed through the stages of labor, we can predict hourly support staff needed. Most inpatient support personnel schedules employ a constant core staff in standard shifts of all eight or all 12-hour employees with constant start and stop times. Significant staff savings can be achieved if the schedules can be made responsive to changes in demand (mixed eight, 10 and 12 hour personnel with staggered start times for each day of the week). Depending on an institution's scheduling practice, we anticipate staff support savings on the order of 10 to 30 percent. While a client who does not own capacity might not use this product directly, staff scheduling represents a major source of cost savings for a facility. The client could use the staffing models as benchmarks in determining appropriateness of present cost structures and as the basis for negotiating with hospitals.

Phase 1   |   Phase 2   |   Phase 3  

Introduction   |   Principals of HSE   |   Our Approach   |   Our Solution