Aims: We propose a summarizing measure for outcome indicators, representing the proportion of patients for whom all desired short-term outcomes of care (a 'textbook outcome') is realized. The aim of this study was to investigate hospital variation in the proportion of patients with a 'textbook outcome' after colon cancer resections in the Netherlands. Methods: Patients who underwent a colon cancer resection in 2010 in the Netherlands were included in the Dutch Surgical Colorectal Audit. A textbook outcome was defined as hospital survival, radical resection, no reintervention, no ostomy, no adverse outcome and a hospital stay < 14 days. We calculated the number of hospitals with a significantly higher (positive outlier) or lower (negative outlier) Observed/Expected (O/E) textbook outcome than average. As quality measures may be more discriminative in a low-risk population, analyses were repeated for low-risk patients only. Results: A total of 5582 patients, treated in 82 hospitals were included. Average textbook outcome was 49% (range 26-71%). Eight hospitals were identified as negative outliers. In these hospitals a 'textbook outcome' was realized in 35% vs. 52% in average hospitals (p < 0.01). In a sub-Analysis for low-risk patients, only one additional negative outlier was identified. Conclusions: The textbook outcome, representing the proportion of patients with a perfect hospitalization, gives a simple comprehensive summary of hospital performance, while preventing indicator driven practice. Therewith the 'textbook outcome' is meaningful for patients, providers, insurance companies and healthcare inspectorate.

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Aims: The aim of the study was to assess which factors contribute to postoperative mortality, especially in elderly patients who undergo emergency colon cancer resections, using a nationwide population-based database. Methods: 6,161 patients (1,172 nonelective) who underwent a colon cancer resection in 2010 in the Netherlands were included. Risk factors for postoperative mortality were investigated using a multivariate logistic regression model for different age groups, elective and nonelective patients separately. Results: For both elective and nonelective patients, mortality risk increased with increasing age. For nonelective elderly patients (80+ years), each additional risk factor increased the mortality risk. For a nonelective patient of 80+ years with an American Society of Anesthesiologists score of III+ and a left hemicolectomy or extended resection, postoperative mortality rate was 41% compared with 7% in patients without additional risk factors. Conclusions: For elderly patients with two or more additional risk factors, a nonelective resection should be considered a high-risk procedure with a mortality risk of up to 41%. The results of this study could be used to adequately inform patient and family and should have consequences for composing an operative team.@en

Objective. Publicly available information on hospital performance is increasing, with the aim to support consumers when choosing a hospital. Besides general hospital information and information on outcomes of care, there is increasing availability of systematically collected information on experiences of other patients. The aim of this study was to assess the influence of previous patients' experiences relative to other information when choosing a hospital for surgical treatment. Methods. Three hundred thirty-seven patient volunteers and 280 healthy volunteers (response rate of 52.4% and 93.3%, respectively) filled out an Internet-based questionnaire that included an adaptive choice-based conjoint analysis. They were asked to select hospital characteristics they would use for future hospital choice, compare hospitals, and choose the overall best hospital. Based on the respondents' choices, the relative importance (RI) of each hospital characteristic for each respondent was estimated using hierarchical Bayes estimation. Results. Information based on previous patients' experience was considered at least as important as information provided by hospitals. "Report card regarding physician's expertise" had the highest RI (16.83 [15.37-18.30]) followed by "waiting time for outpatient clinic appointment" (14.88 [13.42-16.34]) and "waiting time for surgery" (7.95 [7.12-8.78]). Patient and healthy volunteers considered the same hospital attributes to be important, except that patient volunteers assigned greater importance to "positive judgment about physician communication" (7.65 v. 5.80, P < 0.05) and lower importance to "complications" (2.56 v. 4.22, P < 0.05). Conclusion. Consumers consider patient experience-based information at least as important as hospital-based information. They rely most on information regarding physicians' expertise, waiting time, and physicians' communication when choosing a hospital.

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Objective: To determine if composite measures based on process indicators are consistent with short-term outcome indicators in surgical colorectal cancer care. Design: Longitudinal analysis of consistency between composite measures based on process indicators and outcome indicators for 85 Dutch hospitals. Setting: The Dutch Surgical Colorectal Audit database, the Netherlands. Participants: 4732 elective patients with colon carcinoma and 2239 with rectum carcinoma treated in 85 hospitals were included in the analyses. Main outcome measures: All available process indicators were aggregated into five different composite measures. The association of the different composite measures with risk-adjusted postoperative mortality and morbidity was analysed at the patient and hospital level. Results: At the patient level, only one of the composite measures was negatively associated with morbidity for rectum carcinoma. At the hospital level, a strong negative association was found between composite measures and hospital mortality and morbidity rates for rectum carcinoma (p<0.05), and hospital morbidity rates for colon carcinoma. Conclusions: For individual patients, a high score on the composite measures based on process indicators is not associated with better short-term outcome. However, at the hospital level, a good score on the composite measures based on process indicators was consistent with more favourable risk-adjusted short-term outcome rates.

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Aims: The purpose of this study was to determine how expected mortality based on case-mix varies between colorectal cancer patients treated in non-teaching, teaching and university hospitals, or high, intermediate and low-volume hospitals in the Netherlands. Material and methods: We used the database of the Dutch Surgical Colorectal Audit 2010. Factors predicting mortality after colon and rectum carcinoma resections were identified using logistic regression models. Using these models, expected mortality was calculated for each patient. Results: 8580 patients treated in 90 hospitals were included in the analysis. For colon carcinoma, hospitals' expected mortality ranged from 1.5 to 14%. Average expected mortality was lower in patients treated in high-volume hospitals than in low-volume hospitals (5.0 vs. 4.3%, p < 0.05). For rectum carcinoma, hospitals expected mortality varied from 0.5 to 7.5%. Average expected mortality was higher in patients treated in non-teaching and teaching hospitals than in university hospitals (2.7 and 2.3 vs. 1.3%, p < 0.01). Furthermore, rectum carcinoma patients treated in high-volume hospitals had a higher expected mortality than patients treated in low-volume hospitals (2.6 vs. 2.2% p < 0.05). We found no differences in risk-adjusted mortality. Conclusions: High-risk patients are not evenly distributed between hospitals. Using the expected mortality as an integrated measure for case-mix can help to gain insight in where high-risk patients go. The large variation in expected mortality between individual hospitals, hospital types and volume groups underlines the need for risk-adjustment when comparing hospital performances.

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Objective: To assess the impact of quality of care and other hospital information on patients' choices between hospitals. Methods: 665 former surgical patients were invited to respond to an Internet-based questionnaire including a choice-based conjoint analysis. Each patient was presented with 12 different comparisons of 2 hospitals, with each hospital characterized by 6 attributes containing 2 levels. Hospital attributes were included if frequently reported by patients as most important for future hospital choices. These included both general hospital information (e.g., atmosphere), information on quality of care (e.g., percentage of patients with "textbook outcome"), and surgery-specific information (e.g., possibility for minimally invasive procedure). Hierarchial Bayes estimation was used to estimate the utilities for each attribute level for each patient. Based on the ranges of these utilities, the relative importance of each hospital attribute was determined for each participant as a measure of the impact on patients' choices. Results: 308 (46.3%) questionnaires were available for analysis. Of the hospital attributes that patients considered, surgery-specific information on average had the highest relative importance (25.7 [23.9-27.5]), regardless of gender, age, and education. Waiting time and hospital atmosphere were considered least important. The attribute concerning the percentage of patients with "textbook outcomes" had the second greatest impact (18.3 [16.9-19.6]), which was similar for patients with different adverse outcome experience. Conclusions: Surgery-specific and quality of care information are more important than general information when patients choose between hospitals.@en

Objective: To assess whether patients who experience adverse outcomes during hospitalisation or after discharge differ in the information they would use for future choices of a hospital for surgery compared with patients without any adverse outcomes. Design: Cross-sectional questionnaire study, including questions on (1) adverse outcome occurrence during hospitalisation and after discharge, (2) information patients would use for future hospital choice and (3) priority of information. Setting: Three hospitals in the western part of The Netherlands. Study sample All 2122 patients who underwent elective aorta reconstruction (for treatment of aneurysm), cholecystectomy, colon resection, inguinal hernia repair, oesophageal resection or thyroid surgery in the period 2005e2006, of whom 1329 (62.6%) responded. Results: Patients who experienced postdischarge adverse outcomes intend to use more information items to choose a future hospital (on average 1.6 items more). They more often would use the item on information provision during hospitalisation (OR 2.35 (1.37 to 4.03)) and information on various quality-of-care measures, compared with patients without adverse outcomes. Patients who experienced in-hospital adverse outcomes would not use more information items but more often would use the item on mortality after surgery (OR 1.93 (1.27 to 2.94)) and extended hospital stay (OR 1.61 (1.10 to 2.36)). However, when asked for priority of information, previous treatment in that hospital is mentioned as the most important item by most patients (32%), regardless of adverse outcome occurrence, followed by hospital reputation and waiting time. Conclusions: Adverse outcome experience may change the information patients use (on quality of care) to choose a future hospital.@en

Background: In The Netherlands, health professionals have created a doctor-driven standardised system to report and analyse adverse outcomes (AO). The aim is to improve healthcare by learning from past experiences. The key elements of this system are (1) an unequivocal definition of an adverse outcome, (2) appropriate contextual information and (3) a three-dimensional hierarchical classification system. Objectives First, to assess whether routine doctordriven AO reporting is feasible. Second, to investigate how doctors can learn from AO reporting and analysis to improve the quality of care. Methods: Feasibility was assessed by how well doctors reported AO in the surgical department of a Dutch university hospital over a period of 9 years. AO incidence was analysed per patient subgroup and over time, in a time-trend analysis of three equal 3-year periods. AO were analysed case by case and statistically, to learn lessons from past events. Results: In 19 907 surgical admissions, 9189 AOs were reported: One or more AO in 18.2% of admissions. On average, 55 lessons were learnt each year (in 4.3% of AO). More AO were reported in P3 than P1 (OR 1.39 (1.23e1.57)). Although minor AO increased, fatal AO decreased over time (OR 0.59 (0.45e0.77)). Conclusions Doctor-driven AO reporting is shown to be feasible. Lessons can be learnt from case-by-case analyses of individual AO, as well as by statistical analysis of AO groups and subgroups (illustrated by timetrend analysis), thus contributing to the improvement of the quality of care. Moreover, by standardising AO reporting, data can be compared across departments or hospitals, to generate (confidential) mirror information for professionals cooperating in a peer-review setting.@en

Objective: (1) to estimate the occurrence of postdischarge adverse outcomes in surgical patients and their treatment; (2) to explore determinants during admission that may influence the occurrence of postdischarge adverse outcomes. Design: Four weeks after discharge, patients were contacted by telephone and interviewed about: (1) the occurrence of adverse health outcomes after discharge; (2) their treatment and whether readmission or additional surgery was required. Data on the type of surgery and the occurrence of in-hospital adverse outcomes were taken from the routine reporting system. Setting: Dutch university hospital Study sample: All 2145 surgical patient admissions in 2003, of whom 1960 (91%) agreed to participate. Main outcome measures: Percentage of patient admissions with postdischarge adverse outcomes, by type of treatment. Results: Postdischarge adverse outcomes occurred in 487 patients (25%). Most (76%) of the 554 adverse outcomes were treated by a specialist: 165 (30%) during readmission and 257 (46%) at the outpatient clinic. Postdischarge adverse outcomes were mostly infections (39%). In-hospital adverse outcomes and complex surgical procedures increased the probability for both postdischarge adverse outcomes (odds ratio 1.43 (1.05 to 1.94) and 1.36 (1.02 to 1.82) respectively) and postdischarge adverse outcomes that require readmission (odds ratio 1.59 (1.01 to 2.52) and 1.73 (1.09 to 2.74) respectively). Conclusions: Patients should be informed at discharge that postdischarge adverse outcomes may occur, in particular if the patient had complex surgical procedures or adverse outcomes during hospitalisation. Since infections were the main type of postdischarge adverse outcomes, more attention should be given on wound treatment by patients and infection prevention after discharge.

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Problem: Lack of comparable data on adverse outcomes in hospitalised surgical patients. Design: A Plan-Do-Study-Act (PDSA) cycle to implement and evaluate nationwide uniform reporting of adverse outcomes in surgical patients. Evaluation was done within the Reach Efficacy-Adoption Implementation Maintenance (RE-AIM) framework. Setting: All 109 surgical departments in The Netherlands. Key measures for improvement: Increase in the number of departments implementing the reporting system and exporting data to the national database. Strategies for change: The intervention included (1) a coordinator who could mediate in case of problems; (2) participation of an opinion leader; (3) a predefined plan of action communicated to all departments (including feedback of results during implementation); (4) connection with existing hospital databases; (5) provision of software and a helpdesk; and (6) an instrument based on nationwide standards. Effects of change: Implementation increased from 18% to 34% in 1.5 years. The main reason for not implementing the system was that the Information Computer Technology (ICT) department did not link data with the hospital information system (lack of time, finances, low priority). Only 5% of the departments exported data to the national database. Export of data was hindered mainly by slow implementation of the reporting system (so that departments did not have data to export) and by concerns regarding data quality and public availability of data from individual hospitals. Lessons learned: Hospitals need incentives to realise implementation. Important factors are financial support, sufficient manpower, adequate ICT linkage of data, and clarity with respect to public availability of data.@en

Objective: To assess the effectiveness of routine reporting to identify surgical adverse outcomes in comparison with retrospective medical record review. Design: Independent assessment of two methods applied to one sample. Surgeons and surgical residents routinely reported all adverse outcomes for patients in their care during admission. A trained research assistant, blinded to the surgeons' reporting data, retrospectively reviewed the medical records of selected patients and registered all adverse outcomes identified from paper or electronic patient records. Setting: Dutch university hospital. Study sample: A 5% sample of patients (N = 150) discharged in 2002 was taken; oversampling of patients undergoing reoperations, sick patients (ASA ≥3), and those undergoing technically complex surgery was done to increase the yield of adverse outcomes. Main outcome measures: The number of adverse outcomes identified by each method was compared with the total number identified by either method. This was done both for all adverse outcomes and for serious adverse outcomes. Results: Routine reporting identified fewer adverse outcomes than medical record review (62.5% v 78.2%). Complete agreement was achieved in only 40.7% of adverse outcomes. Routine reporting identified slightly more serious adverse outcomes (84.8% v 79.5% of the total), but this difference was not statistically significant. Extrapolating these results to the total number of admissions in 2002, routine reporting underestimated the annual adverse outcome incidence by 1.8% (increasing from 14.5% to 16.3%) and the incidence of serious adverse outcomes by 0.3% (increasing from 6.9% to 7.2%). Conclusions: Neither method identified all adverse outcomes. Routine reporting underestimated the incidence of minor events but was as accurate as record review in identifying serious adverse outcomes.

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In the Netherlands, all procedures in general surgery are categorized into 12 surgery groups by the Association of Surgeons of the Netherlands. The purpose of this study was to assess whether surgery groups differ in adverse outcome probabilities, to decide whether hospital comparisons on adverse outcomes should be adjusted for differences in surgery groups. All surgical patients in one hospital discharged in 1997-1999 were included. Only the first operation during admission was included, with the assumption that successive operations were treatment of adverse outcomes. To avoid bias, only operations with procedures from the same surgery group were included. A total of 6,025 admissions were included and analyzed by a two-step multilevel analysis. Four surgery groups had fewer admissions with adverse outcomes than expected, and two groups had more. After adjustment for patient and operation characteristics, the remaining variance between surgery groups is still large. Similar results were found when differences in mortality were analyzed. Surgery group can therefore be used to adjust hospital comparisons for differences in surgical procedure mix.@en

Objective. To describe the registration of adverse events and the use of the National Surgical Adverse Event Registration (LHCR) software, to analyse possible bottlenecks and to give possibilities for further implementation. Design. Survey. Methods. A reply form was sent to all 134 surgical departments, followed by a telephone interview in case of unclear or missing information or non-response. The questionnaire asked whether adverse events were registered, how they were registered, and whether the LHCR software had been installed. In terms of the LHCR use, the form included questions on whether a file had been created to import available patient, admission and surgery data from the hospital information system (HIS), whether adverse event data had been entered into the database, and whether any bottlenecks had been encountered. Results. After contact by telephone, the data from all 134 departments were retrieved. Adverse events were registered in 92.5% of the departments, but in 42.7% this was done only on paper. The LHCR was operational in 33 departments (24.6%). The most important limiting factor was the creation of an import file with HIS data. Given the fact that such problems can and have been resolved in hospitals where on-site support was provided, the LHCR could potentially be operational in 55-80% of teaching hospitals and in 60-86% of non-teaching hospitals. Conclusion. Adverse events were registered in almost all surgical departments, but often only on paper. The use of the LHCR was limited mainly by the creation of an import file. There appeared to be enough potential to ultimately achieve the initial goals of a nationwide automated registry of adverse events.@en