The role of calling EMS versus using private transportation in improving the management of stroke in France.

OBJECTIVE: To compare the time from symptom onset to brain imaging between patients calling emergency medical services (EMS) and those using private means for transportation. METHODS: We focused on symptom onset-to-brain imaging times of ≤2 hours and ≤3 hours 30 minutes, assuming a one-hour interval between imaging and thrombolysis. Other variables were the patient's age, gender, stroke type, National Institutes of Health Stroke Scale (NIHSS) score, presence of an on-site stroke unit, and period of symptom onset. Univariate analyses and a hierarchical linear regression model were used, as appropriate, and adjusted for these variables. RESULTS: A total of 1,105 stroke patients (28%) were included in the analyses, 40.6% of them transported by EMS. Patients using EMS were significantly older (72.8 vs. 70.5 years; p = 0.008), they had a higher NIHSS score (8 vs. 6.1; p = 0.0001), fewer were ischemic (85.1% vs. 90.6%; p = 0.005), and more of them reached hospitals with an on-site stroke unit (81.3% vs. 72.9%; p = 0.002). For the EMS-call patients, the median symptom onset-to-brain imaging time was significantly shorter (3 hours 21 minutes vs. 5 hours 57 minutes), and after adjustment, maximum delays of 2 hours and 3 hours 30 minutes were independently associated with EMS call: 28% vs. 18% (p = 0.015) and 66% vs. 45% (p < 0.0001) of patients, respectively, leading to an adjusted odds ratio of 2.77 (95% confidence interval, 2.007-3.828; p < 0.0001) for the threshold of 3 hours 30 minutes. CONCLUSIONS: The symptom onset-to-brain imaging time was significantly shorter in case of EMS transportation, but most patients did not reach the hospital in time to be eligible for thrombolysis. Efforts are still needed to reduce delays, especially public education and EMS activation. These efforts should be combined with new approaches for the quality management of stroke patients.

Method for developing national quality indicators based on manual data extraction from medical records.

Developing quality indicators (QI) for national purposes (eg, public disclosure, paying-for-performance) highlights the need to find accessible and reliable data sources for collecting standardised data. The most accurate and reliable data source for collecting clinical and organisational information still remains the medical record. Data collection from electronic medical records (EMR) would be far less burdensome than from paper medical records (PMR). However, the development of EMRs is costly and has suffered from low rates of adoption and barriers of usability even in developed countries. Currently, methods for producing national QIs based on the medical record rely on manual extraction from PMRs. We propose and illustrate such a method. These QIs display feasibility, reliability and discriminative power, and can be used to compare hospitals. They have been implemented nationwide in France since 2006. The method used to develop these QIs could be adapted for use in large-scale programmes of hospital regulation in other, including developing, countries.

Development and feasibility of a set of quality indicators relative to the timeliness and organisation of care for new breast cancer patients undergoing surgery.

BACKGROUND: Because breast cancer is a major public health issue, it is particularly important to measure the quality of the care provided to patients. Survival rates are affected by the timeliness of care, and waiting times constitute key quality criteria. The aim of this study was to develop and validate a set of quality indicators (QIs) relative to the timeliness and organisation of care in new patients with infiltrating, non-inflammatory and metastasis-free breast cancer undergoing surgery. The ultimate aim was to use these QIs to compare hospitals. METHODS: The method of QI construction and testing was developed by COMPAQ-HPST. We first derived a set of 8 QIs from consensus guidelines with the aid of experts and professional associations and then tested their metrological properties in a panel of 60 volunteer hospitals. We assessed feasibility using a grid exploring 5 dimensions, discriminatory power using the Gini coefficient as a measure of dispersion, and inter-observer reliability using the Kappa coefficient. RESULTS: Overall, 3728 records were included in the analyses. All 8 QIs showed acceptable feasibility (but one QI was subject to misinterpretation), fairly strong agreement between observers (Kappa = 0.66), and wide variations in implementation among hospitals (Gini coefficient < 0.45 except for QI 6 (patient information)). They are thus suitable for use to compare hospitals and measure quality improvement. CONCLUSIONS: Of the 8 QIs, 3 are ready for nationwide implementation (time to surgery, time to postoperative multidisciplinary team meeting (MDTM), conformity of MDTM). Four are suitable for use only in hospitals offering surgery with on-site postoperative treatment (waiting time to first appointment after surgery, patient information, time to first postoperative treatment, and traceability of information relating to prognosis). Currently, in the French healthcare system, a patient receives cancer care from different institutions whose databases cannot as yet be easily merged. Nationwide implementation of QIs covering the entire care pathway will thus be a challenge.

Constructing a composite quality score for the care of acute myocardial infarction patients at discharge: impact on hospital ranking.

OBJECTIVE: To determine the impact on hospital ranking of different aggregation methods when creating a composite score from a set of quality indicators relating to a single clinical condition. DESIGN: The analysis was based on 14966 medical records taken from all French hospitals that treated over 30 patients with acute myocardial infarction in 2008 (n=275). Five quality indicators measuring the quality of care delivered to patients with acute myocardial infarction at hospital discharge were aggregated by 5 methods issued from a variety of activity sectors (indicator average, all-or-none, budget allocation process, benefit of the doubt, and unobserved component model). MAIN OUTCOME MEASURES: Each aggregation method was used to rank hospitals into 3 categories depending on the position of the 95% confidence interval of the composite score relative to the overall mean. Variations in rank according to method were estimated using weighted κ coefficients. RESULTS: Agreement between methods ranged from poor (κ=0.20) to almost perfect (κ=0.84). A change of method led to a change in rank for 71% (196 of 275) of hospitals. Only 14 of 121 hospitals which were ranked top and 20 of 118 which were ranked bottom, by at least 1 of the 5 methods, held their rank on a switch to the 4 other methods. CONCLUSION: Hospital ranking varied widely according to 5 aggregation methods. If one method has to be chosen, for instance for reporting to governments, regulatory agencies, payers, health care professionals, and the public, it is necessary to provide its rationale and characteristics, and information on score uncertainty.