Potentially Inappropriate Medication Dispensing in Outpatients: Comparison of Different Measurement Approaches.

Purpose of the Research: This paper aims at comparing different approaches to measure potentially inappropriate medication (PIM) with routinely collected data on prescriptions, patient age institutionalization status (ie in nursing home or in the community). A secondary objective is to measure the rate and prevalence of PIM dispensing and to identify problematic practices in Switzerland. Material and Methods: The studied population includes about 90,000 insured over 17 years old from a Swiss health maintenance organization in 2019 and 2020. We computed and compared the number of PIM per patient for Beers criteria, Priscus list, Laroche, NORGEP and Prescrire approaches. We also created a composite indicator that accounts for the specificities of the Swiss context (adaptation to the Swiss drugs' market, recommendations in force related to sleeping pills, anxiolytics and NSAIDs). We also stratified the analysis per physician, including initiation and cessation of PIM prescription. Results: Our comparison revealed similarities between the approaches, but also that each of them had specific gaps that provides further motivation for the development of a composite approach. PIM rate was particularly high for sleeping pills, anxiolytics, NSAIDs, even when analyses were limited to chronic use. Drugs with anticholinergic effect were also frequently prescribed. Based on our composite indicator, 27% of insured over 64 years old received at least one PIM in 2020, and 8% received more than one. Our analyses also reveal that for sleeping pills and anxiolytics, half of the volume (or prevalence?) occurs in the <65 population. We observed strong variations between physicians and a significant proportion of new users among patients with PIM. Conclusion: Our results show that PIMs prescribing is very frequent in Switzerland and is driven mostly by a few drug categories. There is important physician variation in PIM prescribing that warrants the development of intervention targeted at high PIM-prescribers.

Measuring medically unjustified hospitalizations in Switzerland.

BACKGROUND: Inappropriate use of acute hospital beds is a major topic in health politics. We present here a new approach to measure unnecessary hospitalizations in Medicine and Pediatrics. METHODS: The necessity of a hospital admission was determined using explicit criteria related to the recorded diagnoses. Two indicators (i.e. "unjustified" and "sometimes justified" stays) were applied to more than 800,000 hospital stays and a random sample of 200 of them was analyzed by two clinicians, using routine data available in medical statistics. The validation of the indicators focused on their precision, validity and adjustment, as well as their usefulness (i.e. interest and risk of abuse). RESULTS: Rates, adjusted for case mix (i.e. age of patient, admission planned or not), showed statistically significant differences among hospitals. Only 6.5% of false positives were observed for "unjustified stays" and 17% for "sometimes justified stays". Respectively 7 and 12% of stays had an unknown status, due to a lack of sufficiently precise data. Considering true positives only, almost one third of medical and pediatric stays were classified as not strictly justified from a medical point of view in Switzerland. Among these stays, about one fifth could have probably been avoided without risk. To enable a larger ambulatory shift, recommendations were made to strengthen the ambulatory care, notably regarding post-emergency follow-up, cardiac and pulmonary functions' monitoring, pain management, falls prevention, and specialized at-home services that should be offered. CONCLUSION: We recommend using "unjustified stays" and "sometimes justified stays" indicators to monitor inappropriate hospitalizations. The latter could help the planning of reinforced ambulatory care measures to pursue the ambulatory shift. Nonetheless, we clearly advise against the use of these two indicators for hospitals financing purposes.

Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage.

INTRODUCTION: Postpartum hemorrhage remains a leading cause of maternal morbidity and mortality worldwide. Therefore, cumulative incidence of postpartum hemorrhage and severe postpartum hemorrhage are commonly monitored within and compared across maternity hospitals or countries for obstetrical safety improvement. These indicators are usually based on hospital discharge data though their accuracy is seldom assessed. We aimed to measure postpartum hemorrhage and severe postpartum hemorrhage using electronic health records and hospital discharge data separately and compare the detection accuracy of these methods to manual chart review, and to examine the temporal trends in cumulative incidence of these potentially avoidable adverse outcomes. MATERIALS AND METHODS: We analyzed routinely collected data of 7904 singleton deliveries from a large Swiss university hospital for a three year period (2014-2016). We identified postpartum hemorrhage and severe postpartum hemorrhage in electronic health records by text mining discharge letters and operative reports and calculating drop in hemoglobin from laboratory tests. Diagnostic and procedure codes were used to identify cases in hospital discharge data. A sample of 334 charts was reviewed manually to provide a reference-standard and evaluate the accuracy of the other detection methods. RESULTS: Sensitivities of detection algorithms based on electronic health records and hospital discharge data were 95.2% (95% CI: 92.6% 97.8%) and 38.2% (33.3% to 43.0%), respectively for postpartum hemorrhage, and 87.5% (85.2% to 89.8%) and 36.2% (26.3% to 46.1%) for severe postpartum hemorrhage. Postpartum hemorrhage cumulative incidence based on electronic health records decreased from 15.6% (13.1% to 18.2%) to 8.5% (6.7% to 10.5%) from the beginning of 2014 to the end of 2016, with an average of 12.5% (11.8% to 13.3%). The cumulative incidence of severe postpartum hemorrhage remained at approximately 4% (3.5% to 4.4%). Hospital discharge data-based algorithms provided significantly underestimated incidences. CONCLUSIONS: Hospital discharge data is not accurate enough to assess the incidence of postpartum hemorrhage at hospital or national level. Instead, automated algorithms based on structured and textual data from electronic health records should be considered, as they provide accurate and timely estimates for monitoring and improvement in obstetrical safety. Furthermore, they have the potential to better code for postpartum hemorrhage thus improving hospital reimbursement.

A new statistical methodology overcame the defects of the Bland-Altman method.

BACKGROUND AND OBJECTIVES: The Bland and Altman's limits of agreement (LoA) method is the most commonly used statistical method to assess the bias and precision of a new measuring device (it has been cited over 40,000 times as of March 2019). What is less known is that the LoA method can be dramatically misleading. METHODS: A new statistical methodology, which circumvents these deficiencies, has recently been published and made available in the R and Stata statistical packages. We aimed at introducing and illustrating with a small data set on blood pressure (BP) measurements, taken by two different oscillometric devices, the use of this new methodology to a clinical audience. RESULTS: For diastolic BP, the LoA method was particularly misleading, as it identified differential and proportional biases of opposite signs compared with the new methodology. Regarding systolic BP, the LoA method strongly overestimated both the differential and proportional biases, for both devices. CONCLUSION: The LoA method may be dramatically misleading and does not allow one to estimate the precision of each measurement method. We recommend the use of the newly developed statistical methodology instead.

Adverse events related to hospital care: a retrospective medical records review in a Swiss hospital.

OBJECTIVE: Retrospective records reviews carried out in several countries have shown substantial rates of adverse events (AE) among hospitalized patients, preventable in half the cases. As no such data have been recorded in Switzerland, we estimated the incidence of AE in one acute care hospital as a basis for a safety improvement program. DESIGN: A two steps retrospective records review (screening criteria and full review of positively screened records). SETTING: A medium size community hospital. PARTICIPANTS: A stratified sample of 400 surgical and 600 medical hospitalizations whose records fulfilled a set of information quality criteria. INTERVENTION(S): Not applicable. MAIN OUTCOME MEASURE(S): Adverse events, preventable adverse events and extent of resulting harm. RESULTS: The proportion of hospitalizations with at least one AE was 12.3% (95% CI: 10.4-14.1) whereas the overall hospital incidence rate was 14.1% (95% CI: 12.0-16.2). Nearly half of AE were judged preventable, corresponding to one or more preventable AE in 6.4% of hospitalizations (95% CI: 5.0-7.8). Sixty percent of AE resulted in no or minor impairment at discharge whereas 23% resulted in severe disability. AE were twice more frequent in surgical patients, and preventable AE resulted more often in severe impairment than unpreventable AE. No death was attributed to an AE. The proportion of stays with an AE increased with age and length of stay. CONCLUSIONS: The incidence of preventable AE in patients hospitalized in one Swiss hospital is comparable to previously reported rates. Further, patient safety improvement is needed, especially among older patients, and for surgical procedures.

The effect of patient, provider and financing regulations on the intensity of ambulatory physical therapy episodes: a multilevel analysis based on routinely available data.

BACKGROUND: Many studies have found considerable variations in the resource intensity of physical therapy episodes. Although they have identified several patient- and provider-related factors, few studies have examined their relative explanatory power. We sought to quantify the contribution of patients and providers to these differences and examine how effective Swiss regulations are (nine-session ceiling per prescription and bonus for first treatments). METHODS: Our sample consisted of 87,866 first physical therapy episodes performed by 3,365 physiotherapists based on referrals by 6,131 physicians. We modeled the number of visits per episode using a multilevel log linear regression with crossed random effects for physiotherapists and physicians and with fixed effects for cantons. The three-level explanatory variables were patient, physiotherapist and physician characteristics. RESULTS: The median number of sessions was nine (interquartile range 6-13). Physical therapy use increased with age, women, higher health care costs, lower deductibles, surgery and specific conditions. Use rose with the share of nine-session episodes among physiotherapists or physicians, but fell with the share of new treatments. Geographical area had no influence. Most of the variance was explained at the patient level, but the available factors explained only 4% thereof. Physiotherapists and physicians explained only 6% and 5% respectively of the variance, although the available factors explained most of this variance. Regulations were the most powerful factors. CONCLUSION: Against the backdrop of abundant physical therapy supply, Swiss financial regulations did not restrict utilization. Given that patient-related factors explained most of the variance, this group should be subject to closer scrutiny. Moreover, further research is needed on the determinants of patient demand.

Comparing potentially avoidable hospitalization rates related to ambulatory care sensitive conditions in Switzerland: the need to refine the definition of health conditions and to adjust for population health status.

BACKGROUND: Regional rates of hospitalization for ambulatory care sensitive conditions (ACSC) are used to compare the availability and quality of ambulatory care but the risk adjustment for population health status is often minimal. The objectives of the study was to examine the impact of more extensive risk adjustment on regional comparisons and to investigate the relationship between various area-level factors and the properly adjusted rates. METHODS: Our study is an observational study based on routine data of 2 million anonymous insured in 26 Swiss cantons followed over one or two years. A binomial negative regression was modeled with increasingly detailed information on health status (age and gender only, inpatient diagnoses, outpatient conditions inferred from dispensed drugs and frequency of physician visits). Hospitalizations for ACSC were identified from principal diagnoses detecting 19 conditions, with an updated list of ICD-10 diagnostic codes. Co-morbidities and surgical procedures were used as exclusion criteria to improve the specificity of the detection of potentially avoidable hospitalizations. The impact of the adjustment approaches was measured by changes in the standardized ratios calculated with and without other data besides age and gender. RESULTS: 25% of cases identified by inpatient main diagnoses were removed by applying exclusion criteria. Cantonal ACSC hospitalizations rates varied from to 1.4 to 8.9 per 1,000 insured, per year. Morbidity inferred from diagnoses and drugs dramatically increased the predictive performance, the greatest effect found for conditions linked to an ACSC. More visits were associated with fewer PAH although very high users were at greater risk and subjects who had not consulted at negligible risk. By maximizing health status adjustment, two thirds of the cantons changed their adjusted ratio by more than 10 percent. Cantonal variations remained substantial but unexplained by supply or demand. CONCLUSION: Additional adjustment for health status is required when using ACSC to monitor ambulatory care. Drug-inferred morbidities are a promising approach.

Disease identification based on ambulatory drugs dispensation and in-hospital ICD-10 diagnoses: a comparison.

BACKGROUND: Pharmacy-based case mix measures are an alternative source of information to the relatively scarce outpatient diagnoses data. But most published tools use national drug nomenclatures and offer no head-to-head comparisons between drugs-related and diagnoses-based categories. The objective of the study was to test the accuracy of drugs-based morbidity groups derived from the World Health Organization Anatomical Therapeutic Chemical Classification of drugs by checking them against diagnoses-based groups. METHODS: We compared drugs-based categories with their diagnoses-based analogues using anonymous data on 108,915 individuals insured with one of four companies. They were followed throughout 2005 and 2006 and hospitalized at least once during this period. The agreement between the two approaches was measured by weighted kappa coefficients. The reproducibility of the drugs-based morbidity measure over the 2 years was assessed for all enrollees. RESULTS: Eighty percent used a drug associated with at least one of the 60 morbidity categories derived from drugs dispensation. After accounting for inpatient under-coding, fifteen conditions agreed sufficiently with their diagnoses-based counterparts to be considered alternative strategies to diagnoses. In addition, they exhibited good reproducibility and allowed prevalence estimates in accordance with national estimates. For 22 conditions, drugs-based information identified accurately a subset of the population defined by diagnoses. CONCLUSIONS: Most categories provide insurers with health status information that could be exploited for healthcare expenditure prediction or ambulatory cost control, especially when ambulatory diagnoses are not available. However, due to insufficient concordance with their diagnoses-based analogues, their use for morbidity indicators is limited.

Test result-based sampling: an efficient design for estimating the accuracy of patient safety indicators.

OBJECTIVE: Accuracy studies of Patient Safety Indicators (PSIs) are critical but limited by the large samples required due to low occurrence of most events. We tested a sampling design based on test results (verification-biased sampling [VBS]) that minimizes the number of subjects to be verified. METHODS: We considered 3 real PSIs, whose rates were calculated using 3 years of discharge data from a university hospital and a hypothetical screen of very rare events. Sample size estimates, based on the expected sensitivity and precision, were compared across 4 study designs: random and VBS, with and without constraints on the size of the population to be screened. RESULTS: Over sensitivities ranging from 0.3 to 0.7 and PSI prevalence levels ranging from 0.02 to 0.2, the optimal VBS strategy makes it possible to reduce sample size by up to 60% in comparison with simple random sampling. For PSI prevalence levels below 1%, the minimal sample size required was still over 5000. CONCLUSIONS: Verification-biased sampling permits substantial savings in the required sample size for PSI validation studies. However, sample sizes still need to be very large for many of the rarer PSIs.

Determinants of generic drug substitution in Switzerland.

BACKGROUND: Since generic drugs have the same therapeutic effect as the original formulation but at generally lower costs, their use should be more heavily promoted. However, a considerable number of barriers to their wider use have been observed in many countries. The present study examines the influence of patients, physicians and certain characteristics of the generics' market on generic substitution in Switzerland. METHODS: We used reimbursement claims' data submitted to a large health insurer by insured individuals living in one of Switzerland's three linguistic regions during 2003. All dispensed drugs studied here were substitutable. The outcome (use of a generic or not) was modelled by logistic regression, adjusted for patients' characteristics (gender, age, treatment complexity, substitution groups) and with several variables describing reimbursement incentives (deductible, co-payments) and the generics' market (prices, packaging, co-branded original, number of available generics, etc.). RESULTS: The overall generics' substitution rate for 173,212 dispensed prescriptions was 31%, though this varied considerably across cantons. Poor health status (older patients, complex treatments) was associated with lower generic use. Higher rates were associated with higher out-of-pocket costs, greater price differences between the original and the generic, and with the number of generics on the market, while reformulation and repackaging were associated with lower rates. The substitution rate was 13% lower among hospital physicians. The adoption of the prescribing practices of the canton with the highest substitution rate would increase substitution in other cantons to as much as 26%. CONCLUSIONS: Patient health status explained a part of the reluctance to substitute an original formulation by a generic. Economic incentives were efficient, but with a moderate global effect. The huge interregional differences indicated that prescribing behaviours and beliefs are probably the main determinant of generic substitution.

Surgical safety and hospital volume across a wide range of interventions.

OBJECTIVES: For certain major operations, inpatient mortality risk is lower in high-volume hospitals than those in low-volume hospitals. Extending the analysis to a broader range of interventions and outcomes is necessary before adopting policies based on minimum volume thresholds. METHODS: Using the United States 2004 Nationwide Inpatient Sample, we assessed the effect of intervention-specific and overall hospital volume on surgical complications, potentially avoidable reoperations, and deaths across 1.4 million interventions in 353 hospitals. Outcome variations across hospitals were analyzed through a 3-level hierarchical logistic regression model (patients, surgical interventions, and hospitals), which took into account interventions on multiple organs, 144 intervention categories, and structural hospital characteristics. Discriminative performance and calibration were good. RESULTS: Hospitals with more experience in a given intervention had similar reoperation rates but lower mortality and complication rates: odds ratio per volume deciles 0.93 and 0.97. However, the benefit was limited to heart surgery and a small number of other operations. Risks were higher for hospitals that performed more interventions overall: odds ratio per 1000 for each event was approximately 1.02. Even after adjustment for specific volume, mortality varied substantially across both high- and low-volume hospitals. CONCLUSION: Although the link between specific volume and certain inpatient outcomes suggests that specialization might help improve surgical safety, the variable magnitude of this link and the heterogeneity of hospital effect do not support the systematic use of volume-based referrals. It may be more efficient to monitor risk-adjusted postoperative outcomes and to investigate facilities with worse than expected outcomes.

Cross-national comparative performance of three versions of the ICD-10 Charlson index.

OBJECTIVE: The Charlson comorbidity index has been widely used for risk adjustment in outcome studies using administrative health data. Recently, 3 International Statistical Classification of Diseases, Tenth Revision (ICD-10) translations have been published for the Charlson comorbidities. This study was conducted to compare the predictive performance of these versions (the Halfon, Sundararajan, and Quan versions) of the ICD-10 coding algorithms using data from 4 countries. METHODS: Data from Australia (N = 2000-2001, max 25 diagnosis codes), Canada (N = 2002-2003, max 16 diagnosis codes), Switzerland (N = 1999-2001, unlimited number of diagnosis codes), and Japan (N = 2003, max 11 diagnosis codes) were analyzed. Only the first admission for patients age 18 years and older, with a length of stay of >/=2 days was included. For each algorithm, 2 logistic regression models were fitted with hospital mortality as the outcome and the Charlson individual comorbidities or the Charlson index score as independent variables. The c-statistic (representing the area under the receiver operating characteristic curve) and its 95% probability bootstrap distribution were employed to evaluate model performance. RESULTS: Overall, within each population's data, the distribution of comorbidity level categories was similar across the 3 translations. The Quan version produced slightly higher median c-statistics than the Halfon or Sundararajan versions in all datasets. For example, in Japanese data, the median c-statistics were 0.712 (Quan), 0.709 (Sundararajan), and 0.694 (Halfon) using individual comorbidity coefficients. In general, the probability distributions between the Quan and the Sundararajan versions overlapped, whereas those between the Quan and the Halfon version did not. CONCLUSIONS: Our analyses show that all of the ICD-10 versions of the Charlson algorithm performed satisfactorily (c-statistics 0.70-0.86), with the Quan version showing a trend toward outperforming the other versions in all data sets.

Risk-adjusted rates for potentially avoidable reoperations were computed from routine hospital data.

OBJECTIVES: Reoperations may reflect a suboptimal initial surgical treatment. The study aimed to develop a screening algorithm for those potentially avoidable, using only routinely collected hospital data and a prediction model to adjust rates for case-mix. STUDY DESIGN AND SETTING: Data of a 3-year random sample of 7,370 therapeutic operations on inpatients, among which 833 were followed-up by a reoperation during the same stay. A review of medical records identified clearly avoidable and other potentially avoidable reoperations to develop and test the screening algorithm. A logistic prediction model of potentially avoidable reoperations was developed on one randomly chosen half of the data (about 9,000 interventions) and tested on the other half (cross-validation). RESULTS: Two hundred thirty-seven interventions (3%) were followed by a potentially avoidable reoperation, among which 144 were clearly avoidable. The screening algorithm had a sensitivity of 75% and a specificity of 72%. Predictors of potentially avoidable reoperations were surgery categories, diagnosis related conditions, and experiencing prior surgery. The risk score, based on these variables, showed at once a satisfactory discriminative performance (C-statistic=0.76) and goodness-of-fit measure on the validation set. CONCLUSION: The adjusted rate of potentially avoidable reoperations should be included in internal reporting of hospital quality indicators, but further validated in various settings.

Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care.

BACKGROUND: The hospital readmission rate has been proposed as an important outcome indicator computable from routine statistics. However, most commonly used measures raise conceptual issues. OBJECTIVES: We sought to evaluate the usefulness of the computerized algorithm for identifying avoidable readmissions on the basis of minimum bias, criterion validity, and measurement precision. RESEARCH DESIGN AND SUBJECTS: A total of 131,809 hospitalizations of patients discharged alive from 49 hospitals were used to compare the predictive performance of risk adjustment methods. A subset of a random sample of 570 medical records of discharge/readmission pairs in 12 hospitals were reviewed to estimate the predictive value of the screening of potentially avoidable readmissions. MEASURES: Potentially avoidable readmissions, defined as readmissions related to a condition of the previous hospitalization and not expected as part of a program of care and occurring within 30 days after the previous discharge, were identified by a computerized algorithm. Unavoidable readmissions were considered as censored events. RESULTS: A total of 5.2% of hospitalizations were followed by a potentially avoidable readmission, 17% of them in a different hospital. The predictive value of the screen was 78%; 27% of screened readmissions were judged clearly avoidable. The correlation between the hospital rate of clearly avoidable readmission and all readmissions rate, potentially avoidable readmissions rate or the ratio of observed to expected readmissions were respectively 0.42, 0.56 and 0.66. Adjustment models using clinical information performed better. CONCLUSION: Adjusted rates of potentially avoidable readmissions are scientifically sound enough to warrant their inclusion in hospital quality surveillance.

A methodology to estimate the potential to move inpatient to one day surgery.

BACKGROUND: The proportion of surgery performed as a day case varies greatly between countries. Low rates suggest a large growth potential in many countries. Measuring the potential development of one day surgery should be grounded on a comprehensive list of eligible procedures, based on a priori criteria, independent of local practices. We propose an algorithmic method, using only routinely available hospital data to identify surgical hospitalizations that could have been performed as one day treatment. METHODS: Moving inpatient surgery to one day surgery was considered feasible if at least one surgical intervention was eligible for one day surgery and if none of the following criteria were present: intervention or affection requiring an inpatient stay, patient transferred or died, and length of stay greater than four days. The eligibility of a procedure to be treated as a day case was mainly established on three a priori criteria: surgical access (endoscopic or not), the invasiveness of the procedure and the size of the operated organ. Few overrides of these criteria occurred when procedures were associated with risk of immediate complications, slow physiological recovery or pain treatment requiring hospital infrastructure. The algorithm was applied to a random sample of one million inpatient US stays and more than 600 thousand Swiss inpatient stays, in the year 2002. RESULTS: The validity of our method was demonstrated by the few discrepancies between the a priori criteria based list of eligible procedures, and a state list used for reimbursement purposes, the low proportion of hospitalizations eligible for one day care found in the US sample (4.9 versus 19.4% in the Swiss sample), and the distribution of the elective procedures found eligible in Swiss hospitals, well supported by the literature. There were large variations of the proportion of candidates for one day surgery among elective surgical hospitalizations between Swiss hospitals (3 to 45.3%). CONCLUSION: The proposed approach allows the monitoring of the proportion of inpatient stay candidates for one day surgery. It could be used for infrastructure planning, resources negotiation and the surveillance of appropriate resource utilization.

Identifying priorities in methodological research using ICD-9-CM and ICD-10 administrative data: report from an international consortium.

BACKGROUND: Health administrative data are frequently used for health services and population health research. Comparative research using these data has been facilitated by the use of a standard system for coding diagnoses, the International Classification of Diseases (ICD). Research using the data must deal with data quality and validity limitations which arise because the data are not created for research purposes. This paper presents a list of high-priority methodological areas for researchers using health administrative data. METHODS: A group of researchers and users of health administrative data from Canada, the United States, Switzerland, Australia, China and the United Kingdom came together in June 2005 in Banff, Canada to discuss and identify high-priority methodological research areas. The generation of ideas for research focussed not only on matters relating to the use of administrative data in health services and population health research, but also on the challenges created in transitioning from ICD-9 to ICD-10. After the brain-storming session, voting took place to rank-order the suggested projects. Participants were asked to rate the importance of each project from 1 (low priority) to 10 (high priority). Average ranks were computed to prioritise the projects. RESULTS: Thirteen potential areas of research were identified, some of which represented preparatory work rather than research per se. The three most highly ranked priorities were the documentation of data fields in each country's hospital administrative data (average score 8.4), the translation of patient safety indicators from ICD-9 to ICD-10 (average score 8.0), and the development and validation of algorithms to verify the logic and internal consistency of coding in hospital abstract data (average score 7.0). CONCLUSION: The group discussions resulted in a list of expert views on critical international priorities for future methodological research relating to health administrative data. The consortium's members welcome contacts from investigators involved in research using health administrative data, especially in cross-jurisdictional collaborative studies or in studies that illustrate the application of ICD-10.

Ambulatory healthcare information system: a conceptual framework.

Despite the tremendous amount of data collected in the field of ambulatory care, political authorities still lack synthetic indicators to provide them with a global view of health services utilization and costs related to various types of diseases. Moreover, public health indicators fail to provide useful information for physicians' accountability purposes. The approach is based on the Swiss context, which is characterized by the greatest frequency of medical visits in Europe, the highest rate of growth for care expenditure, poor public information but a lot of structured data (new fee system introduced in 2004). The proposed conceptual framework is universal and based on descriptors of six entities: general population, people with poor health, patients, services, resources and effects. We show that most conceptual shortcomings can be overcome and that the proposed indicators can be achieved without threatening privacy protection, using modern cryptographic techniques. Twelve indicators are suggested for the surveillance of the ambulatory care system, almost all based on routinely available data: morbidity, accessibility, relevancy, adequacy, productivity, efficacy (from the points of view of the population, people with poor health, and patients), effectiveness, efficiency, health services coverage and financing. The additional costs of this surveillance system should not exceed Euro 2 million per year (Euro 0.3 per capita).

Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

OBJECTIVES: Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. METHODS: ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. RESULTS: Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. CONCLUSIONS: These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.

Measuring potentially avoidable hospital readmissions.

The objectives of this study were to develop a computerized method to screen for potentially avoidable hospital readmissions using routinely collected data and a prediction model to adjust rates for case mix. We studied hospital information system data of a random sample of 3,474 inpatients discharged alive in 1997 from a university hospital and medical records of those (1,115) readmitted within 1 year. The gold standard was set on the basis of the hospital data and medical records: all readmissions were classified as foreseen readmissions, unforeseen readmissions for a new affection, or unforeseen readmissions for a previously known affection. The latter category was submitted to a systematic medical record review to identify the main cause of readmission. Potentially avoidable readmissions were defined as a subgroup of unforeseen readmissions for a previously known affection occurring within an appropriate interval, set to maximize the chance of detecting avoidable readmissions. The computerized screening algorithm was strictly based on routine statistics: diagnosis and procedures coding and admission mode. The prediction was based on a Poisson regression model. There were 454 (13.1%) unforeseen readmissions for a previously known affection within 1 year. Fifty-nine readmissions (1.7%) were judged avoidable, most of them occurring within 1 month, which was the interval used to define potentially avoidable readmissions (n = 174, 5.0%). The intra-sample sensitivity and specificity of the screening algorithm both reached approximately 96%. Higher risk for potentially avoidable readmission was associated with previous hospitalizations, high comorbidity index, and long length of stay; lower risk was associated with surgery and delivery. The model offers satisfactory predictive performance and a good medical plausibility. The proposed measure could be used as an indicator of inpatient care outcome. However, the instrument should be validated using other sets of data from various hospitals.