Determine the degree of congruence between several measures of adverse events. Cross-sectional study to assess frequency and type of adverse events identified using a variety of methods. Mayo Clinic Rochester hospitals. All inpatients discharged in 2005 (n = 60 599). Adverse events were identified through multiple methods: (i) Agency for Healthcare Research and Quality-defined patient safety indicators (PSIs) using ICD-9 diagnosis codes from administrative discharge abstracts, (ii) provider-reported events, and (iii) Institute for Healthcare Improvement Global Trigger Tool with physician confirmation. PSIs were adjusted to exclude patient conditions present at admission. Agreement of identification between methods. About 4% (2401) of hospital discharges had an adverse event identified by at least one method. Around 38% (922) of identified events were provider-reported events. Nearly 43% of provider-reported adverse events were skin integrity events, 23% medication events, 21% falls, 1.8% equipment events and 37% miscellaneous events. Patients with adverse events identified by one method were not usually identified using another method. Only 97 (6.2%) of hospitalizations with a PSI also had a provider-reported event and only 10.5% of provider-reported events had a PSI. Different detection methods identified different adverse events. Findings are consistent with studies that recommend combining approaches to measure patient safety for internal quality improvement. Potential reported adverse event inconsistencies, low association with documented harm and reporting differences across organizations, however, raise concerns about using these patient safety measures for public reporting and organizational performance comparison.

To determine the inter-rater reliability of the Institute for Healthcare Improvement's Global Trigger Tool (GTT) in a practice setting, and explore the value of individual triggers. Prospective assessment of application of the GTT to monthly random samples of hospitalized patients at four hospitals across three regions in the USA. Mayo Clinic campuses are in Minnesota, Arizona and Florida. A total of 1138 non-pediatric inpatients from all units across the hospital. GTT was applied to randomly selected medical records with independent assessments of two registered nurses with a physician review for confirmation. The Cohen Kappa coefficient was used as a measure of inter-rater agreement. The positive predictive value was assessed for individual triggers. Good levels of reliability were obtained between independent nurse reviewers at the case-level for both the occurrence of any trigger and the identification of an adverse event. Nurse reviewer agreement for individual triggers was much more varied. Higher agreement appears to occur among triggers that are objective and consistently recorded in selected portions of the medical record. Individual triggers also varied on their yield to detect adverse events. Cases with adverse events had significantly more triggers identified (mean 4.7) than cases with no adverse events (mean 1.8). The trigger methodology appears to be a promising approach to the measurement of patient safety. However, automated processes could make the process more efficient in identifying adverse events and has a greater potential of improving care delivery and patient ‘outcomes’.

Research addressing value in healthcare requires a measure of cost. While there are many sources and types of cost data, each has strengths and weaknesses. Many researchers appear to create study-specific cost datasets, but the explanations of their costing methodologies are not always clear, causing their results to be difficult to interpret. Our solution, described in this paper, was to use widely accepted costing methodologies to create a service-level, standardized healthcare cost data warehouse from an institutional perspective that includes all professional and hospital-billed services for our patients. The warehouse is based on a National Institutes of Research–funded research infrastructure containing the linked health records and medical care administrative data of two healthcare providers and their affiliated hospitals. Since all patients are identified in the data warehouse, their costs can be linked to other systems and databases, such as electronic health records, tumor registries, and disease or treatment registries. We describe the two institutions’ administrative source data; the reference files, which include Medicare fee schedules and cost reports; the process of creating standardized costs; and the warehouse structure. The costing algorithm can create inflation-adjusted standardized costs at the service line level for defined study cohorts on request. The resulting standardized costs contained in the data warehouse can be used to create detailed, bottom-up analyses of professional and facility costs of procedures, medical conditions, and patient care cycles without revealing business-sensitive information. After its creation, a standardized cost data warehouse is relatively easy to maintain and can be expanded to include data from other providers. Individual investigators who may not have sufficient knowledge about administrative data do not have to try to create their own standardized costs on a project-by-project basis because our data warehouse generates standardized costs for defined cohorts upon request.

Background: Mitigating or reducing the risk of harm associated with the delivery of healthcare is a policy priority. While the risk of harm can be reduced in some instances (i.e. preventable), what constitutes preventable harm remains unclear. A standardized and clear definition of preventable harm is the first step towards safer and more efficient healthcare delivery system. We aimed to summarize the definitions of preventable harm and its conceptualization in healthcare. Methods: We conducted a comprehensive electronic search of relevant databases from January 2001 to June 2011 for publications that reported a definition of preventable harm. Only English language publications were included. Definitions were coded for common concepts and themes. We included any study type, both original studies and reviews. Two reviewers screened the references for eligibility and 28% (127/460) were finally included. Data collected from studies included study type, description of the study population and setting, and data corresponding to the outcome of interest. Three reviewers extracted the data. The level of agreement between the reviewers was calculated. Results: One hundred and twenty seven studies were eligible. The three most prevalent preventable harms in the included studies were: medication adverse events (33/127 studies, 26%), central line infections (7/127, 6%) and venous thromboembolism (5/127, 4%). Seven themes or definitions for preventable harm were encountered. The top three were: presence of an identifiable modifiable cause (58/132 definitions, 44%), reasonable adaptation to a process will prevent future recurrence (30/132, 23%), adherence to guidelines (22/132, 16%). Data on the validity or operational characteristic (e.g., accuracy, reproducibility) of definitions were limited. Conclusions: There is limited empirical evidence of the validity and reliability of the available definitions of preventable harm, such that no single one is supported by high quality evidence. The most common definition is “presence of an identifiable, modifiable cause of harm”.

Objective: Our objective was to assess the cost implications of changing the intensive care unit staffing model from on-demand presence to mandatory 24-hr in-house critical care specialist presence. Design: A pre-post comparison was undertaken among the prospectively assessed cohorts of patients admitted to our medical intensive care unit 1 yr before and 1 yr after the change. Our data were stratified by Acute Physiology and Chronic Health Evaluation III quartile and whether a patient was admitted during the day or at night. Costs were modeled using a generalized linear model with log-link and γ-distributed errors. Setting: A large academic center in the Midwest. Patients: All patients admitted to the adult medical intensive care unit on or after January 1, 2005 and discharged on or before December 31, 2006. Patients receiving care under both staffing models were excluded. Intervention: Changing the intensive care unit staffing model from on-demand presence to mandatory 24-hr in-house critical care specialist presence. Measurements and Main Results: Total cost estimates of hospitalization were calculated for each patient starting from the day of intensive care unit admission to the day of hospital discharge. Adjusted mean total cost estimates were 61% lower in the post period relative to the pre period for patients admitted during night hours (7 pm to 7 am) who were in the highest Acute Physiology and Chronic Health Evaluation III quartile. No significant differences were seen at other severity levels. The unadjusted intensive care unit length of stay fell in the post period relative to the pre period (3.5 vs. 4.8) with no change in non-intensive care unit length of stay. Conclusions: We find that 24-hr intensive care unit intensivist staffing reduces lengths of stay and cost estimates for the sickest patients admitted at night. The costs of introducing such a staffing model need to be weighed against the potential total savings generated for such patients in smaller intensive care units, especially ones that predominantly care for lower-acuity patients.