A guide to using performance measurement systems for continuous improvement.

The Joint Commission on Accreditation of Healthcare Organizations requires accredited organizations to use a performance measurement system that meets its inclusion requirements to satisfy performance outcome and measurement expectations. The system, known as the ORYX initiative, is used for both internal performance control and external performance comparisons. This article outlines a three-step approach to using a performance measurement system based on the philosophy of continuous improvement and the methods of statistical process control (SPC). SPC, the methodology recommended by the Joint Commission, can be applied to the analysis of many quality measures and can be implemented with Microsoft Excel software.

Using SPC (statistical process control) to analyze measurements in a healthcare organization.

This article presents a tutorial on statistical process control (SPC) for measurement data and the use of trial control charts. Examples from healthcare applications are used to illustrate one process in statistical control and two other processes not in statistical control.

Automated rescreening in cervical cytology. Mathematical models for evaluating overall process sensitivity, specificity and cost.

OBJECTIVE: To develop mathematical models to assist decision makers with the difficult task of evaluating the use of automated rescreening in the process of screening cervical smears. STUDY DESIGN: Using assumptions about incidence, per smear screening costs, and the sensitivity and specificity of cytotechnologists, pathologists and the rescreening device, basic probability models were developed to describe the overall sensitivity, specificity and cost of the screening process. RESULTS: The optimal screening policy is highly dependent on assumptions, and an automated system can significantly affect the overall system cost and accuracy. CONCLUSION: Mathematical planning models are valuable tools to assist decision makers in the design of a screening process for cervical smears.

Using SPC (statistical process control) to analyze quality indicators in a healthcare organization.

A tool of statistical process control (SPC) called a P Chart is introduced as a process-based approach for analyzing quality indicators in healthcare. The underlying methodology of a P Chart is described using card-playing examples. A discussion on the implementation of SPC in healthcare follows, along with examples using Maryland Project quality indicators.

Statistical analysis of data in cervical cytology from the viewpoint of total quality management.

As a consequence of widespread dissatisfaction with the high incidence of false negatives in cytologic smear screening, the Clinical Laboratory Improvement Amendments of 1988 were enacted by Congress with specific requirements for quality assurance in the screening of cytologic smears for cervical cancer. This paper examines the process of cervical cytologic screening from a total quality management perspective and suggests the use of several statistical techniques from industrial total quality management for describing and monitoring the process of cytologic smear screening. Several examples are included, and a general approach to implementing these techniques is suggested.

Rescreening policies in cervical cytology and their effect on detecting the truly positive patient.

In the use of a specific screening policy that relies on the expertise of cytotechnologists and pathologists to examine cytologic smears for the detection of cervical cancer, it is important to know the probability of correctly identifying a positive patient and, for each 1,000 patients, to know the probability distribution, the expected value and the standard deviation of the number of correct identifications. A probability model for the standard 10% rescreening rule mandated by the Clinical Laboratory Improvement Act of 1988 and Medicare was developed and used to evaluate higher screening rates. In addition, the model is applied to an alternative screening policy to study multiple inspections by a specific number of cytotechnologists prior to rescreening by a pathologist. For each policy the probability distribution, expected value and standard deviation of the number of correct identifications per 1,000 positive patients are given.

An economic model for comparing alternative policies for cervical cytologic smear screening.

In this paper a mathematical model is developed to determine the probability that a truly negative cervical cytologic smear will be correctly identified by a system of screening policies that uses one or more cytotechnologists to independently and sequentially prescreen such smears for the detection of cervical cancer. This is an extension of previous work that modeled the probability of detecting a truly positive smear under the same set of policies. In this system any positive reading by a cytotechnologist causes a slide to be rescreened by a pathologist, and if all cytotechnologists declare a slide to be negative, the slide is placed in a pool for random selection of slides to be rescreened by the pathologist. The policy of single screening by a cytotechnologist, with subsequent 10% rescreening of the negative slides, as suggested by the Clinical Laboratory Improvement Amendments of 1988, is thus embedded in the policies that are modeled. In addition, a cost model is developed that takes into account the cost of screening a slide by a cytotechnologist, of rescreening a slide by a pathologist, of a false-positive reading and of a false-negative reading. This cost model can be used to determine which policy is optimal for the parameters that pertain to a specific situation. Examples are presented to illustrate the use of the cost model. The results of a computer simulation model are also presented to validate the mathematical results and to display the variability of total cost.

Sizing free-standing ambulatory care facilities.

This guide, written by an administrator and an industrial engineer, identifies and discusses the factors that should be considered when planning space requirements for ambulatory care facilities. The authors view sizing the ambulatory care facility as a complicated sequential task where trade-offs are made with regard to several factors: philosophy of patient care; cost; expansion requirements; patient comfort and waiting time; patient privacy; staff preferences; utilization patterns; and scurity of the faculty. It is suggested that the weight assigned to each factor when making trade-offs will be largely affected by the philosophy of patient care. The authors divide ambulatory care facilities into five functional groups: (1) basic medical services; (2) supporting medical services; (3) administrations; (4) support and service facilities; and (5) community and secondary support facilities. There are tables summarizing recommended space requirements for each of the functional groups based on mathematical models of the number and type of people using them (i.e., a typical physician is expected to see 24 patients per day). Space requirements for the whole clinic can be determined by adding together those required for each functional component. Provisions for growth and technological advances are discussed. An extensive bibliography is included.

A survey of financial planning models for health care organizations.

This paper describes "what if?" financial planning models developed for health care administrators and financial managers to study and evaluate the economic impact of changes in a health care organization's charge structure, operating policies, reimbursement plans, and services and resources. Models for inpatient and outpatient care systems are presented. The models are described in terms of input, output, and application. An assessment of the state of the art of financial planning and prospects for the future of what if?models are given.