Assessing and controlling pain are very challenging tasks, especially for hospital resident physicians who are responsible for relieving patients' pain without causing harm in people with differing perceptions of pain and differing levels of tolerance to pain and to narcotics. In a pair of reports, Brian Harting and his coauthors describe 2 studies in the use of computer simulation of patients in pain as a tool in improving pain management without causing harm. In reporting on the first study, the authors acknowledge a limitation in that it was carried out at a single institution and that the sample of residents was self-selected. While they did not observe statistically significant improvement in pain scores or use of rescue agents as a result of the experimental training, they conclude that the pain simulator, as a way of demonstrating the pharmacokinetics of narcotic agents, can be an effective training tool. In their second study, the same authors analyzed to what extent lessons learned on the simulator translated into improved pain control during the first 48 hours of care for patients with cancer-related pain crises. The findings suggested that simulator training had indeed translated into improved pain control for these patients.
James E. Rohrer and his coauthors suggest that, in the interest of quality improvement, the detection of objectively high levels of pain should be followed up with definitive action. Such action depends on the availability of effective treatments. The authors point out that, while associations among obesity and pain arising from bone and joint disorders and migraine progression are well documented, the relationship, if any, between obesity per se and general pain has not been investigated. They suggest that if such a relationship were established, treatment of obesity might prove useful in reducing patients' dependence on pain medications. In their study sample, a body mass index >35 was a risk factor for elevated pain scores.
Over the past 20 years, acute inpatient psychiatric care has moved from the traditional state-operated "storage" model custodial setting to the psychiatric unit in a general hospital. Nancy P. Hanrahan and Linda H. Aiken explore the ways in which this change has affected the quality of care, psychiatric nurse staffing, and patient outcomes. Their findings suggest that psychiatric nurses in the general medical-surgical hospital have issues related to job dissatisfaction and burnout, with implications for retention and the quality of psychiatric inpatient care.
Arguing in favor of wider use of cumulative sum (Cusum) control charts in healthcare statistical process control, Marco D. Huesch and his coauthors point to the invalid inferences that may be associated with use of the conventional p chart approach in the presence of serially dependent binary data. They make the point that human-generated data streams are likely to violate the assumption of independence between serial outcomes in a time series, ignoring the fact that a data-generator (a practitioner) may change his or her behavior over time as a result of learning. Despite their observation that type 1 error rates associated with the Cusum chart begin to inflate at very high levels of serial dependence, they support the Cusum approach in analyzing serially dependent binary data in healthcare.
Hae Mi Choe and his coauthors conducted a prospective, pre-post study to explore the effect of a multidisciplinary team approach in treating patients with type 2 diabetes. Their approach was associated with a demonstrated improvement in blood pressure control.
The effect of establishing and following a clinical pathway in caring for patients undergoing laparoscopic prostatectomy is the subject of the report by Cesar Llorente-Parado and his coauthors. They studied changes in duration of the surgical procedure, complication rate, length of hospital stay, use and duration of postoperative medication and catheterization, and patients' courses following discharge from the hospital.
Gregory L. Alexander contends that the enhanced regulatory pressure of the past few years in the nursing home industry has not brought about consistent measurable improvement in organizational performance. Using the Nursing Home Compare national database as a data source, he explores the effects of the deployment patterns of professional nurses, licensed practical nurses, and certified nursing assistants on 14 measures of quality of care derived from the Minimum Data Set. The author recommends use of the Nursing Home Compare database in ongoing research on the organizational factors that influence quality of care of nursing home residents. He also suggests that the use of contemporary electronic information technology may contribute to better care by facilitating better organization of nursing staff time.
Monitoring and changing the quality of patient outcomes are dependent on the availability and use of complete and accurate patient data. Pavani Rangachari explores the relationship between the techniques of collecting and reporting medical information and the effectiveness of efforts to evaluate and improve patient care and offers 8 proposed organizational measures designed to improve the quality of care through better information management.
Jean Gayton Carroll, PhD
Editor