Alan B. Cohen

Evaluation readiness: improved evaluation planning using a data inventory framework☆

Factors intrinsic to many programs, such as ambiguously stated objectives, inadequately defined performance measures, and incomplete or unreliable databases, often conspire to limit the evaluability of these programs. Current evaluation planning approaches are somewhat constrained in their ability to overcome these obstacles and to achieve full preparedness for evaluation. In this paper, the concept of evaluation readiness is introduced as a complement to other evaluation planning approaches, most notably that of evaluability assessment. The basic products of evaluation readiness--the formal program definition and the data inventory framework--are described, along with a guide for assuring more timely and appropriate evaluation response capability to support the decision making needs of program managers. The utility of evaluation readiness for program planning, as well as for effective management, is also discussed.

Certificate of need and low capital-cost medical technology.

years have spawned new medical technologies at a prodigious rate. Swift adoption of these innovations has not only altered the face of medical practice, but has transformed many hospitals into increasingly complex, resource-intensive institutions. In numerous instances, such radical change has hampered objective evaluation of clinical risks and benefits associated with these new technologies. Accompanying this trend there has been a growing concern that the costs of new equipment and procedures may be adding greatly to the inflationary trend seen in health care expenditures (Feldstein and Taylor, 1977; Altman and Wallack, 1979). One study (Abt Associates, 1975) estimated in 1975 that capital costs of major medical equipment alone may contribute 9 percent to the annual rise in hospital expenditures. Warner (1979) subsequently added operating expenses to this figure, calculating that equipment-embodied technologies alone may actually account for nearly 34 percent of the annual cost increase. One public policy response to this problem has been to attempt restraint of technology diffusion to hospitals. The prime policy instruments have been state Certificate of Need (CON) programs, which

Clinical Decision Making in an Era of DRG-Based Prospective Payment

Since 1982, the Congress and the Health Care Financing Administration (HCFA) have taken several important steps toward changing the method of payment to hospitals for inpatient services rendered to Medicare beneficiaries. The first of these steps the Tax Equity and Fiscal Responsibility Act (TEFRA) of 1982 imposed stringent limits on hospital payments in an effort to slow the rate of increase in hospital costs. The subsequent passage of the Social Security Amendments of 1983 reinforced this initiative by calling for the creation of a prospective payment system based on case mix groupings known as Diagnosis Related Groups, or DRGs. The Medicare Prospective Payment System, or PPS, as it has come to be known, entered its second year of operation on October 1, 1984, and is currently being applied in approximately 5,200 hospitals across the nation [1]. Although it is not yet clear what effects, positive or negative, may have resulted from the system’s first year of operation, there is mounting concern in many quarters that the financial incentives engendered by the new system may pose serious implications for the quality of care delivered to Medicare beneficiaries. One such area of concern now being investigated by HCFA is the &dquo;unusual increase&dquo; in the number of Medicare discharges from hospitals that was observed in 1,446 hospitals just prior to the implementation of PPS in 1983 [1]. Of more serious concern, however, may be the implications for clinical practice and for hospital adoption of medical technology raised in several

SMDM's Ninth Annual Meeting

explanations could explain the results. In both studies physicians who preferred a gamble in the gain setting had higher rates of test ordering than physicians who chose the certain gain. While these differences were not statistically significant, the studies’ sample sizes were not large. It is possible that preference for a gamble rather than loss aversion is the attitudinal influence on test ordering. Further studies of physician risk preferences will be of great interest. It will be important to measure risk