OBJECTIVE: To create a means by which we can examine and understand the interrelations among the fundamental elements of hospital inpatient care (patients, beds, theatre time, and staff). DESIGN: Predictive study of resource utilisation based on a computerised clinical information system of five years' audit data from a surgical management system. SETTING: One surgical firm (of one consultant, one registrar, and one preregistration houseman) in a district general hospital. PATIENTS: 5267 Patients whose admission records were part of the five years' audit of surgical management. MAIN OUTCOME MEASURES: Mean length of stay; number of occupied beds; turnover interval; throughput (patients/bed); percentage elective theatre occupancy; waiting time for elective admissions; and theatre, hotel, and total costs. RESULTS: Predicted outcome was analysed in the model, taking the actual outcomes in 1988-9 as baseline values, for four clinical scenarios: an increase in accident and emergency admissions, a reduction in beds, a reduced length of stay, and creation of a new firm. Baseline values showed a mean stay of just over five days in 15 beds and with a theatre occupancy of 94%; the total cost was 812,000 pounds (hotel costs 597,000 pounds). Increasing the accident and emergency admissions to 460/year (19%), based on projected trends from 1984 to 1988, resulted in increased hotel costs (55,000 pounds) and reducing bed numbers (by halving admissions) in decreased use of theatres to 71%, decreased throughput, and increased waiting time, from 20 to 92 weeks, at a saving of 99,000 pounds (12%). Reducing stay marginally reduced bed occupancy (8%) and hotel costs (14%), and creating a new surgical team considerably reduced bed occupancy (14%) and waiting time for elective operations (by 20%). The minimum number of beds for referrals, accident and emergency admissions, and planned admissions was 9.0; that for urgent elective admissions was 3.3 and for non-urgent admissions was 2.4. CONCLUSION: A well designed clinical information system with the routine collection of data can provide the necessary output data to enable resource modelling. IMPLICATION: Use of such a model will allow clinicians to participate in resource planning on the basis of what is actually happening within the hospital.
OBJECTIVE: To create a means by which we can examine and understand the interrelations among the fundamental elements of hospital inpatient care (patients, beds, theatre time, and staff). DESIGN: Predictive study of resource utilisation based on a computerised clinical information system of five years' audit data from a surgical management system. SETTING: One surgical firm (of one consultant, one registrar, and one preregistration houseman) in a district general hospital. PATIENTS: 5267 Patients whose admission records were part of the five years' audit of surgical management. MAIN OUTCOME MEASURES: Mean length of stay; number of occupied beds; turnover interval; throughput (patients/bed); percentage elective theatre occupancy; waiting time for elective admissions; and theatre, hotel, and total costs. RESULTS: Predicted outcome was analysed in the model, taking the actual outcomes in 1988-9 as baseline values, for four clinical scenarios: an increase in accident and emergency admissions, a reduction in beds, a reduced length of stay, and creation of a new firm. Baseline values showed a mean stay of just over five days in 15 beds and with a theatre occupancy of 94%; the total cost was 812,000 pounds (hotel costs 597,000 pounds). Increasing the accident and emergency admissions to 460/year (19%), based on projected trends from 1984 to 1988, resulted in increased hotel costs (55,000 pounds) and reducing bed numbers (by halving admissions) in decreased use of theatres to 71%, decreased throughput, and increased waiting time, from 20 to 92 weeks, at a saving of 99,000 pounds (12%). Reducing stay marginally reduced bed occupancy (8%) and hotel costs (14%), and creating a new surgical team considerably reduced bed occupancy (14%) and waiting time for elective operations (by 20%). The minimum number of beds for referrals, accident and emergency admissions, and planned admissions was 9.0; that for urgent elective admissions was 3.3 and for non-urgent admissions was 2.4. CONCLUSION: A well designed clinical information system with the routine collection of data can provide the necessary output data to enable resource modelling. IMPLICATION: Use of such a model will allow clinicians to participate in resource planning on the basis of what is actually happening within the hospital.