Evaluation of the Determinants of Satisfaction With Postoperative Pain Control After Thoracoscopic Surgery: A Single-Center, Survey-Based Study.

BACKGROUND: The need to measure, compare, and improve the quality of pain management is important to patients, payers, and health care providers. Pain after thoracic surgery can be severe, and thoracoscopic approaches have not had the favorable impact on pain as anticipated. The aim of this study was to evaluate the determinants of patient satisfaction with acute pain management and the effectiveness of pain control after video-assisted thoracoscopic surgery using a modified version of the Revised American Pain Society Patient Outcome Questionnaire. METHODS: We performed a single-center, prospective, survey-based study of 300 patients who had undergone elective video-assisted thoracoscopic surgery. Patients were enrolled and completed the survey on postoperative day 1 or 2. The primary outcome variable was patient-reported satisfaction with acute postoperative pain treatment measured on a 1-4 scale. The relationship between the items on the survey and patient satisfaction was analyzed to determine the factors significantly associated with satisfaction. RESULTS: Fifty-one percent of the patients had the highest satisfaction level with pain treatment, and 4% of the patients had the lowest satisfaction level. The mean reported acceptable pain level was 3.8 ± 1.9 (numeric rating scale [NRS], 0-10). The average pain intensity score at the time of the survey was 2.8 ± 2.1 (NRS, 0-10). The median for the most pain in the prior 24 hours was 7 (NRS, 0-10; interquartile range, 5-9). Five items from the survey were significantly associated with the satisfaction level. The predictor with the highest associated odds ratio (OR) with satisfaction was the ability to participate in pain management decisions (OR, 1.45; P < .0001). Another positively associated predictor was receiving helpful information about pain treatment options (OR, 1.31; P = .002). Negatively associated predictors of patient satisfaction included level of pain intensity at time of survey (OR, 0.76; P = .002), lowest pain score in the prior 24 hours (OR, 0.70; P = .0006), and having pain interfere with sleep in the postoperative period (OR, 0.72; P = .037). CONCLUSIONS: Our findings highlight several factors associated with patient satisfaction with acute postoperative pain management. Interventions focused on achieving acceptable pain levels for the majority of the time, ensuring that patients are able to get sleep, providing patients with helpful information about their pain treatment, and, most importantly, allowing patients to participate in decisions about their pain management may improve patient satisfaction with postoperative pain management.

CREATING AND AUDITING A NEW ELECTRONIC CONTINUOUS INFUSION PRESCRIPTION CHART FOR A PAEDIATRIC CRITICAL CARE UNIT.

INTRODUCTION: Prescription errors, including continuous infusion prescriptions are one major source of concern in the paediatric population. Evidence suggests that use of an electronic or web-based calculator could minimise these errors. In our paediatric critical care unit (PCCU) we have created an electronic continuous infusion prescription chart to target errors in this area and conducted an audit to assess its effect on error reduction. AIM: To create an electronic continuous infusion prescription chart and audit its effect on prescription errors. METHOD: Similar electronic continuous infusion prescription charts were evaluated. A Choice of electronic formats were considered and excel was chosen for its simplicity and flexibility. The choice of medications to be included, dilution method, and dosage range was agreed between PCCU consultant, pharmacy and nursing staff. Formulas for calculating each medication infusion was created and validated for different age and weight ranges by at least 2 PCCU trained pharmacists, accounting for capping at certain age and weight bands as appropriate for the medication. These were programmed into the spreadsheet for automatic calculation using inputted age and weight for the selected medications. Continuous infusion prescriptions were audited 6 months before and after implementation in April 2015 of this electronic chart. Parameters audited include medication dose, infusion rate, concentration, route, legibility, and missing or incorrect patient details. A trial period of 4 weeks preceded implementation. RESULTS: The electronic continuous infusion prescription form was created and used on PCCU. Hand written prescriptions had higher error rate (30.7%) as compared to electronic charts (0.7%) with a p-value <0.002. No errors were found in electronic prescriptions in regards to dose, volume and rate calculation. DISCUSSION AND CONCLUSION: The use of an electronic continuous infusion prescription chart has been successfully set up and used on PCCU. Its use has significantly reduced continuous infusion prescription error rates. The one error on electronic prescription charts was due to incorrect data input.Whilst similar formats exist for transferring patients between intensive care units in the UK, this differs by its use on inpatients. As a new project, various learning points were gained during the process. Some discrepancies in the formulas were identified during the validation process and trial period and the flexibility to change these quickly was paramount. The need to standardise prescribing habits and administration preferences was also important before proceeding to the formulation stage. Security and version control was another factor to consider ensuring restricted use of the most updated version.Major advantages of this prescription chart include ease of set up and low cost compared to established commercial programs. Another was the ability to quickly adapt information to the changing needs of the unit or updated dosage recommendations.In summary, the use of the electronic continuous infusion prescription chart has significantly reduced prescription error rates on PCCU. It has also allowed more efficient use of medical and pharmacy time resources.

Paediatric electronic infusion calculator: An intervention to eliminate infusion errors in paediatric critical care.

INTRODUCTION: Medication errors, including infusion prescription errors are a major public health concern, especially in paediatric patients. There is some evidence that electronic or web-based calculators could minimise these errors. AIMS: To evaluate the impact of an electronic infusion calculator on the frequency of infusion errors in the Paediatric Critical Care Unit of The Royal London Hospital, London, United Kingdom. METHOD: We devised an electronic infusion calculator that calculates the appropriate concentration, rate and dose for the selected medication based on the recorded weight and age of the child and then prints into a valid prescription chart. Electronic infusion calculator was implemented from April 2015 in Paediatric Critical Care Unit. A prospective study, five months before and five months after implementation of electronic infusion calculator, was conducted. Data on the following variables were collected onto a proforma: medication dose, infusion rate, volume, concentration, diluent, legibility, and missing or incorrect patient details. RESULTS: A total of 132 handwritten prescriptions were reviewed prior to electronic infusion calculator implementation and 119 electronic infusion calculator prescriptions were reviewed after electronic infusion calculator implementation. Handwritten prescriptions had higher error rate (32.6%) as compared to electronic infusion calculator prescriptions (<1%) with a p < 0.001. Electronic infusion calculator prescriptions had no errors on dose, volume and rate calculation as compared to handwritten prescriptions, hence warranting very few pharmacy interventions. CONCLUSIONS: Use of electronic infusion calculator for infusion prescription significantly reduced the total number of infusion prescribing errors in Paediatric Critical Care Unit and has enabled more efficient use of medical and pharmacy time resources.

The Value of Performance Measurement in Promoting Improvements in Women's Health.

OBJECTIVES: To determine the factors associated with the use and impact of performance data relevant to women's health. METHODS: We developed a survey on six levels of information use based on Knott and Wildavsky's (1980) policy utilization framework and used this survey to determine Ontario hospital administrators' use of women's health report indicators. We related responses to this survey to six potentially relevant organizational factors, such as women's health as a written hospital priority, a women's health program and hospital budget size, using correlation and multiple-regression analysis. RESULTS: Only women's health in a written hospital priority (p=0.01) and hospital budget (p=0.02, log transformed) were significantly associated with the highest level of use when all organizational factors were considered. CONCLUSION: These findings suggest that the use of women's health performance indicators is strongly related to the size of the hospital budget and to organizational commitment to women's health.

A comparison of systemwide and hospital-specific performance measurement tools.

Balanced scorecards are being implemented at the system and organizational levels to help managers link their organizational strategies with performance data to better manage their healthcare systems. Prior to this study, hospitals in Ontario, Canada, received two editions of the system-level scorecard (SLS)--a framework, based on the original balanced scorecard, that includes four quadrants: system integration and management innovation (learning and growth), clinical utilization and outcomes (internal processes), patient satisfaction (customer), and financial performance and condition (financial). This study examines the uptake of the SLS framework and indicators into institution-specific scorecards for 22 acute care institutions and 2 non-acute-care institutions. This study found that larger (teaching and community) hospitals were significantly more likely to use the SLS framework to report performance data than did small hospitals (p < 0.0049 and 0.0507) and that teaching hospitals used the framework significantly more than community hospitals did (p < 0.0529). The majority of hospitals in this study used at least one indicator from the SLS in their own scorecards. However, all hospitals in the study incorporated indicators that required data collection and analysis beyond the SLS framework. The study findings suggest that SLS may assist hospitals in developing institution-specific scorecards for hospital management and that the balanced scorecard model can be modified to meet the needs of a variety of hospitals. Based on the insight from this study and other activities that explore top priorities for hospital management, the issues related to efficiency and human resources should be further examined using SLSs.