Monitoring and improving the productivity of semi-autonomous human service units

Many of the organizations that provide human services at the local level are quite automonous in terms of their operations, but rely on the funds and some services provided by program managers at federal, state, and local levels. Simple direct comparison is difficult due to differences in local populations, service mixes, and resource bases. Yet program managers have a responsibility to allocate their limited resources effectively, to monitor and evaluate performance, and to offer assistance in improving program productivity and program impact. In this study, the family planning programs of 77 local health departments in the state of North Carolina were first classified by a technique somewhat like the process of analytical review that is currently of interest to the accounting and auditing professions. Linear regression models were developed to relate the family planning programs’ outputs to program funding levels and the nature of the populations being served. This adjusted the observed productivity for level and quality of resource inputs and for process variables represented by the nature of the community and the scale of operations, all major problems in productivity measurement for service systems. The resulting regression model was used as a predictor of individual program productivity. The study then proceeded to classify the local programs into those whose actual performances were substantially above, near, or substantially below these predicted performance levels. A subset of the programs substantially above and substantially below predicted performance were visited to identify operating system differences, including the staffing pattern used, service work station arrangement and client flows, staff and supervisory attitudes, and program innovations that appeared to be associated with relative productivity differences. These empirical results in high and lower performing service units are very consistent with what we have learned about productivity in other types of organizations, e.g., the companies cited in In Search of Excellence, and Japanese manufacturing operations. Recommendations were then prepared relating to several areas of possible intervention by higher level program managers in terms of training needs, consultation on procedures and policies, and considerations in the process of allocating resources. Such an approach appears to have considerable promise as a way of approaching the task of monitoring and improving the efficiency and effectiveness of the operations of decentralized and relatively autonomous service units. Even though the nature of inputs could explain 70-76% of the variability in output, those units that were high in output (controlled for inputs) did exhibit significant and interesting differences from those that were low in output (controlled for input). Most of these differences come back to basic management attitudes-flexibility, innovativeness commitment, communication with staff, risk-taking and willingness to set and work toward high performance standards.