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Vanessa B Hurley,1 Yue Wang,2 Hector P Rodriguez,3 Stephen M Shortell,3 Stephen Kearing,4 Lucy A Savitz5 1Health Systems Administration, Georgetown University, Washington, DC 20057, USA; 2Tile, San Mateo, CA 94403, USA; 3Health Policy and Management, University of California, Berkeley School of Public Health, Berkeley, CA 94720, USA; 4High Value Healthcare Collaborative, Hanover, NH 03755, USA; 5Center for Health Research (Northwest and Hawaii), Health Research, Kaiser Permanente, Portland, OR 97227, USACorrespondence: Vanessa B HurleyHealth Systems Administration, Georgetown University, St. Mary’s Hall 231, 3700 Reservoir Road, NW, Washington, DC 20057, USATel +1 202-687-4209Email vh151@georgetown.eduBackground: Shared decision making (SDM) research has emphasized the role of decision aids (DAs) for helping patients make treatment decisions reflective of their preferences, yet there have been few collaborative multi-institutional efforts to integrate DAs in orthopedic consultations and primary care encounters.Objective: In the context of routine DA implementation for SDM, we investigate which patient-level characteristics are associated with patient preferences for surgery versus medical management before and after exposure to DAs. We explored whether DA implementation in primary care encounters was associated with greater shifts in patients’ treatment preferences after exposure to DAs compared to DA implementation in orthopedic consultations.Design: Retrospective cohort study.Setting: 10 High Value Healthcare Collaborative (HVHC) health systems.Study participants: A total of 495 hip and 1343 adult knee osteoarthritis patients who were exposed to DAs within HVHC systems between July 2012 to June 2015.Results: Nearly 20% of knee patients and 17% of hip patients remained uncertain about their treatment preferences after viewing DAs. Older patients and patients with high pain levels had an increased preference for surgery. Older patients receiving DAs from three HVHC systems that transitioned DA implementation from orthopedics into primary care had lower odds of preferring surgery after DA exposure compared to older patients in seven HVHC systems that only implemented DAs for orthopedic consultations.Conclusion: Patients’ treatment preferences were largely stable over time, highlighting that DAs for SDM largely do not necessarily shift preferences. DAs and SDM processes should be targeted at older adults and patients reporting high pain levels. Initiating treatment conversations in primary versus specialty care settings may also have important implications for engagement of patients in SDM via DAs.Keywords: shared decision making, patient engagement, patient preferences, quality of care, health systems, collaborative learning

Objective: To examine response rate information from mailed physician questionnaires reported in published articles.Data Sources/study Setting: Citations for articles published between 1985 and 1995 were obtained using a key word search of the Medline, PsychLit, and Sociofile databases.Study Design: A 5 percent random sample of relevant citations was selected from each year.Data Collection/extraction Methods: Citations found to be other than physician surveys were discarded and replaced with the next randomly assigned article. Selected articles were abstracted using a standardized variable list.Principal Findings: The average response rate for mailed physician questionnaires was 61 percent. The average response rate for large sample surveys (> 1,000 observations) was 52 percent. In addition, only 44 percent of the abstracted articles reported a discussion of response bias, and only 54 percent reported any type of follow-up.Conclusions: (1) Response rates have remained somewhat constant over time, and (2) researchers need to document the efforts used to increase response rates to mailed physician questionnaires. [ABSTRACT FROM AUTHOR]

Kimberly D Brunisholz,1,2,* Pascal Briot,1,2,* Sharon Hamilton,1 Elizabeth A Joy,3 Michael Lomax,2 Nathan Barton,2 Ruthann Cunningham,3 Lucy A Savitz,2 Wayne Cannon1 1Primary Care Clinical Program, Intermountain Healthcare, Salt Lake City, UT, USA; 2Institute for Healthcare Delivery, Intermountain Healthcare, Salt Lake City, UT, USA; 3Office of Research, Intermountain Healthcare, Salt Lake City, UT, USA*Joint first authors Purpose: The purpose of this study was to determine the impact of diabetes self-management education (DSME) in improving processes and outcomes of diabetes care as measured by a five component diabetes bundle and HbA1c, in individuals with type 2 diabetes mellitus (T2DM). Methods: A retrospective analysis was performed for adult T2DM patients who received DSME training in 2011–2012 from an accredited American Diabetes Association center at Intermountain Healthcare (IH) and had an HbA1c measurement within the prior 3 months and 2–6 months after completing their first DSME visit. Control patients were selected from the same clinics as case-patients using random number generator to achieve a 1 to 4 ratio. Case and control patients were included if 1) pre-education HbA1c was between 6.0%–14.0%; 2) their main provider was a primary care physician; 3) they met the national Healthcare Effectiveness Data and Information Set criteria for inclusion in the IH diabetes registry. The IH diabetes bundle includes retinal eye exam, nephropathy screening or prescription of angiotensin converting enzyme or angiotensin receptor blocker; blood pressure

Focuses on the methodological issues affecting the quality of perinatal health care in the U.S. Role of women and social function after child delivery; Development of a population-based focus on the health status of communities; Aspects of mother-infant pair; Clinical risk factors of patient population.

Promotion of colorectal cancer (CRC) screening can be expensive and unnecessary for many patients. The use of predictive analytics promises to help health systems target the right services to the right patients at the right time while improving population health. Multilevel data at the interpersonal, organizational, community, and policy levels, is rarely considered in clinical decision making but may be used to improve CRC screening risk prediction. We compared the effectiveness of a CRC screening risk prediction model that uses multilevel data with a more conventional model that uses only individual patient data. We used a retrospective cohort to ascertain the one-year occurrence of CRC screening. The cohort was determined from a Health Maintenance Organization, in Oregon. Eligible patients were 50-75 years old, health plan members for at least one year before their birthday in 2018 and were due for screening. We created a risk model using logistic regression first with data available in the electronic health record (EHR), and then added multilevel data. In a cohort of 59,249 patients, 36.1% completed CRC screening. The individual level model included 14 demographic, clinical and encounter based characteristics, had a bootstrap-corrected C-statistic of 0.722 and sufficient calibration. The multilevel model added 9 variables from clinical setting and community characteristics, and the bootstrap-corrected C-statistic remained the same with continued sufficient calibration. The predictive power of the CRC screening model did not improve after adding multilevel data. Our findings suggest that multilevel data added no improvement to the prediction of the likelihood of CRC screening., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Introduction: A mission-critical aspect of learning health systems (LHSs) is the provision of evidence-based practice. One source of such evidence is provided by the Agency for Healthcare Research and Quality (AHRQ) through rigorous systematic reviews, termed evidence reports that synthesize available evidence on nominated topics of interest. However, the AHRQ Evidence-based Practice Center (EPC) program recognizes that the production of high-quality evidence reviews does not guarantee or promote their use and usability in practice., Methods: To make these reports more relevant to LHSs and promote evidence dissemination, AHRQ awarded a contract to the American Institutes for Research (AIR) and its Kaiser Permanente ACTION (KPNW ACTION) partner to design and implement web-based tools to meet the gap in dissemination and implementation of EPC reports in LHSs. We used a co-production approach to accomplish this work across three phases of activity: planning, co-design, and implementation between 2018 and 2021. We describe the methods and results and discuss implications for future efforts., Results: Web-based information tools that provide clinically relevant summaries with clear visual representations from the AHRQ EPC systematic evidence reports may be used by LHSs to increase awareness and accessibility of EPC reports, formalize and enhance LHSs' evidence review infrastructure, develop system-specific protocols and care pathways, improve practice at the point of care, and train and educate., Conclusions: The co-design of these tools and facilitated implementation generated an approach to making EPC reports more accessible and allows for more widespread application of systematic review results in supporting evidence-based practices in LHSs., Competing Interests: None of the investigators have any affiliations or financial involvement that conflicts with the material presented in this report., (© 2022 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

The benefits of supporting experiential learning for improved health and societal outcomes have been recognized in many countries. A number of funding organizations have developed competitive funding opportunities to support experiential learning in health system organizations outside of the traditional university setting. AcademyHealth in the US is an early innovator that pioneered the Delivery System Science Fellowship (DSSF) and inspired Canada's creation of the Health System Impact (HSI) Fellowship program. The DSSF and HSI Fellowship have similar objectives: to improve the career readiness of doctorally prepared graduates and to build research capacity within health system organizations. However, the programs have taken different approaches to achieve these objectives and operate in different healthcare systems. This paper outlines the two models of embedded fellowships, analyzes their commonalities and differences, discusses lessons learned and suggests future directions for health services and policy research training., (Copyright © 2019 Longwoods Publishing.)

The dissemination of evidence-based best practice through the entire health care system remains an elusive goal, despite public pressure and regulatory guidance. Many patients do not receive the same quality of care at different hospitals across the same health care system. We describe the role of a data driven learning collaborative, the High Value Healthcare Collaborative (HVHC), in the dissemination of best practice using adherence to the 3-hour-bundle for sepsis care. Compliance with and adoption of sepsis bundle care elements comparing sites with mature vs non-mature care delivery processes were measured during the improvement effort for a cohort of 20,758 patients. Non-mature sites increased their bundle compliance from 71.0 to 86.7 percent (p < 0.005). This compliance increase was primarily based on increased compliance with the fluid element of the bundle that improved for non-mature locations from 76.4 to 94.0 percent (p < 0.005).

Research Objective: Determine whether hospitals are increasing the duration of observation stays following index admission for heart failure to avoid potential payment penalties from the Hospital Readmission Reduction Program., Study Design: The Hospital Readmission Reduction Program applies a 30-day cutoff after which readmissions are no longer penalized. Given this seemingly arbitrary cutoff, we use regression discontinuity design, a quasi-experimental research design that can be used to make causal inferences., Population Studied: The High Value Healthcare Collaborative includes member healthcare systems covering 57% of the nation's hospital referral regions. We used Medicare claims data including all patients residing within these regions. The study included patients with index admissions for heart failure from January 1, 2012 to June 30, 2015 and a subsequent observation stay within 60 days. We excluded hospitals with fewer than 25 heart failure readmissions in a year or fewer than 5 observation stays in a year and patients with subsequent observation stays at a different hospital., Principal Findings: Overall, there was no discontinuity at the 30-day cutoff in the duration of observation stays, the percent of observation stays over 12 hours, or the percent of observation stays over 24 hours. In the sub-analysis, the discontinuity was significant for non-penalized., Conclusion: The findings reveal evidence that the HRRP has resulted in an increase in the duration of observation stays for some non-penalized hospitals.

The last in a series of four papers on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review describes how delivery system science provides a systematic means to answer questions that arise in translating complex interventions to other practice settings. When the focus is on translation and spread of innovations, the questions are different than in evaluative research. Causal inference is not the main issue, but rather one must ask: How and why does the intervention work? What works for whom and in what contexts? How can a model be amended to work in new settings? In these settings, organizational factors and design, infrastructure, policies, and payment mechanisms all influence an intervention's success, so a theory-driven formative evaluation approach that considers the full path of the intervention from activities to engage participants and change how they act to the expected changes in clinical processes and outcomes is needed. This requires a scientific approach to quality improvement that is characterized by a basis in theory; iterative testing; clear, measurable process and outcomes goals; appropriate analytic methods; and documented results. To better answer the questions that arise in delivery system science, this paper introduces a number of standard qualitative research approaches that can be applied in a learning health system: Pawson and Tilley's "realist evaluation," theory-based evaluation approaches, mixed-methods and case study research approaches, and the "positive deviance" approach.

Learning health systems use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning. Even without randomization, observational studies can play a central role as the nation's health care system embraces comparative effectiveness research and patient-centered outcomes research. However, neither the breadth, timeliness, volume of the available information, nor sophisticated analytics, allow analysts to confidently infer causal relationships from observational data. However, depending on the research question, careful study design and appropriate analytical methods can improve the utility of EHD. The introduction to a series of four papers, this review begins with a discussion of the kind of research questions that EHD can help address, noting how different evidence and assumptions are needed for each. We argue that when the question involves describing the current (and likely future) state of affairs, causal inference is not relevant, so randomized clinical trials (RCTs) are not necessary. When the question is whether an intervention improves outcomes of interest, causal inference is critical, but appropriately designed and analyzed observational studies can yield valid results that better balance internal and external validity than typical RCTs. When the question is one of translation and spread of innovations, a different set of questions comes into play: How and why does the intervention work? How can a model be amended or adapted to work in new settings? In these "delivery system science" settings, causal inference is not the main issue, so a range of quantitative, qualitative, and mixed research designs are needed. We then describe why RCTs are regarded as the gold standard for assessing cause and effect, how alternative approaches relying on observational data can be used to the same end, and how observational studies of EHD can be effective complements to RCTs. We also describe how RCTs can be a model for designing rigorous observational studies, building an evidence base through iterative studies that build upon each other (i.e., confirmation across multiple investigations).

The second paper in a series on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review summarizes study design approaches, including choosing appropriate data sources, and methods for design and analysis of natural and quasi-experiments. The primary strength of study design approaches described in this section is that they study the impact of a deliberate intervention in real-world settings, which is critical for external validity. These evaluation designs address estimating the counterfactual - what would have happened if the intervention had not been implemented. At the individual level, epidemiologic designs focus on identifying situations in which bias is minimized. Natural and quasi-experiments focus on situations where the change in assignment breaks the usual links that could lead to confounding, reverse causation, and so forth. And because these observational studies typically use data gathered for patient management or administrative purposes, the possibility of observation bias is minimized. The disadvantages are that one cannot necessarily attribute the effect to the intervention (as opposed to other things that might have changed), and the results do not indicate what about the intervention made a difference. Because they cannot rely on randomization to establish causality, program evaluation methods demand a more careful consideration of the "theory" of the intervention and how it is expected to play out. A logic model describing this theory can help to design appropriate comparisons, account for all influential variables in a model, and help to ensure that evaluation studies focus on the critical intermediate and long-term outcomes as well as possible confounders.

AcademyHealth's Delivery System Science Fellowship (DSSF) provides a paid postdoctoral pragmatic learning experience to build capacity within learning healthcare systems to conduct research in applied settings. The fellowship provides hands-on training and professional leadership opportunities for researchers. Since its inception in 2012, the program has grown rapidly, with 16 health systems participating in the DSSF to date. In addition to specific projects conducted within health systems (and numerous publications associated with those initiatives), the DSSF has made several broader contributions to the field, including defining delivery system science, identifying a set of training objectives for researchers working in delivery systems, and developing a national collaborative network of care delivery organizations, operational leaders, and trainees. The DSSF is one promising approach to support higher-value care by promoting continuous learning and improvement in health systems., (© 2017 Society of Hospital Medicine.)

Importance: The value of integrated team delivery models is not firmly established., Objective: To evaluate the association of receiving primary care in integrated team-based care (TBC) practices vs traditional practice management (TPM) practices (usual care) with patient outcomes, health care utilization, and costs., Design: A retrospective, longitudinal, cohort study to assess the association of integrating physical and mental health over time in TBC practices with patient outcomes and costs., Setting and Participants: Adult patients (aged ≥18 years) who received primary care at 113 unique Intermountain Healthcare Medical Group primary care practices from 2003 through 2005 and had yearly encounters with Intermountain Healthcare through 2013, including some patients who received care in both TBC and TPM practices., Exposures: Receipt of primary care in TBC practices compared with TPM practices for patients treated in internal medicine, family practice, and geriatrics practices., Main Outcomes and Measures: Outcomes included 7 quality measures, 6 health care utilization measures, payments to the delivery system, and program investment costs., Results: During the study period (January 2010-December 2013), 113,452 unique patients (mean age, 56.1 years; women, 58.9%) accounted for 163,226 person-years of exposure in 27 TBC practices and 171,915 person-years in 75 TPM practices. Patients treated in TBC practices compared with those treated in TPM practices had higher rates of active depression screening (46.1% for TBC vs 24.1% for TPM; odds ratio [OR], 1.91 [95% CI, 1.75 to 2.08), adherence to a diabetes care bundle (24.6% for TBC vs 19.5% for TPM; OR, 1.26 [95% CI, 1.11 to 1.42]), and documentation of self-care plans (48.4% for TBC vs 8.7% for TPM; OR, 5.59 [95% CI, 4.27 to 7.33]), lower proportion of patients with controlled hypertension (<140/90 mm Hg) (85.0% for TBC vs 97.7% for TPM; OR, 0.87 [95% CI, 0.80 to 0.95]), and no significant differences in documentation of advanced directives (9.6% for TBC vs 9.9% for TPM; OR, 0.97 [95% CI, 0.91 to 1.03]). Per 100 person-years, rates of health care utilization were lower for TBC patients compared with TPM patients for emergency department visits (18.1 for TBC vs 23.5 for TPM; incidence rate ratio [IRR], 0.77 [95% CI, 0.74 to 0.80]), hospital admissions (9.5 for TBC vs 10.6 for TPM; IRR, 0.89 [95% CI, 0.85 to 0.94]), ambulatory care sensitive visits and admissions (3.3 for TBC vs 4.3 for TPM; IRR, 0.77 [95% CI, 0.70 to 0.85]), and primary care physician encounters (232.8 for TBC vs 250.4 for TPM; IRR, 0.93 [95% CI, 0.92 to 0.94]), with no significant difference in visits to urgent care facilities (55.7 for TBC vs 56.2 for TPM; IRR, 0.99 [95% CI, 0.97 to 1.02]) and visits to specialty care physicians (213.5 for TBC vs 217.9 for TPM; IRR, 0.98 [95% CI, 0.97 to 0.99], P > .008). Payments to the delivery system were lower in the TBC group vs the TPM group ($3400.62 for TBC vs $3515.71 for TPM; β, -$115.09 [95% CI, -$199.64 to -$30.54]) and were less than investment costs of the TBC program., Conclusions and Relevance: Among adults enrolled in an integrated health care system, receipt of primary care at TBC practices compared with TPM practices was associated with higher rates of some measures of quality of care, lower rates for some measures of acute care utilization, and lower actual payments received by the delivery system.

Introduction: Intermountain Healthcare is a fully integrated delivery system based in Salt Lake City, Utah. As a learning healthcare system with a mission of performance excellence, it became apparent that population health management and our efforts to move towards shared accountability would require additional patient-centric metrics in order to provide the right care to the right patients at the right time. Several European countries have adopted social deprivation indices in measuring the impact that social determinants can have on health. Such indices provide a geographic, area-based measure of how socioeconomically deprived residents of that area are on average. Intermountain's approach was to identify a proxy measure that did not require front-line data collection and could be standardized for our patient population, leading us to the area deprivation index or ADI. This paper describes the specifications and calculation of an ADI for the state of Utah. Results are presented along with introduction of three use cases demonstrating the potential for application of an ADI in quality improvement in a learning healthcare system., Case Description: The Utah ADI shows promise in providing a proxy for patient-reported measures reflecting key socio-economic indicators useful for tailoring patient interventions to improve health care delivery and patient outcomes. Strengths of this approach include a consistent standardized measurement of social determinants, use of more granular block group level measures and a limited data capture burden for front-line teams. While the methodology is generalizable to other communities, results of this index are limited to block groups within the state of Utah and will differ from national calculations or calculations for other states. The use of composite measures to evaluate individual characteristics must also be approached with care. Other limitations with the use of U.S. Census data include use of estimates and missing data., Conclusion: Initial applications in three meaningfully different areas of an integrated health system provide initial evidence of its broad applicability in addressing the impact of social determinants on health. The variation in socio-economic status by quintile also has potential for clinical significance, though more research is needed to link variation in ADI with variation in health outcomes overall and by disease type.

Executive Summary: Oregon's coordinated care organizations (CCOs) are an integral part of a massive statewide reform that brings accountable care to Medicaid. CCOs are regional collaboratives among health plans, providers, county public health, and communitybased organizations that administer a single global budget covering physical, mental, and dental healthcare for low-income Oregonians. CCOs have been given freedom within the global budget to implement reforms that might capture efficiencies in cost and quality. For this study-fielded between 2012 and 2015-we traced the path of the global budget through the interior structures of two of Oregon's most promising CCOs. Using document review and in-depth qualitative interviews, we synthesized and summarized descriptive narrative data to produce case studies of the financial models in each CCO. We found that the CCOs feature substantially different market contexts, governance models, organizational structures, and financial systems.

Background and Objectives: Gaps exist in inpatient asthma care. Our aims were to assess the impact of an evidence-based care process model (EB-CPM) 5 years after implementation at Primary Children's Hospital (PCH), a tertiary care facility, and after its dissemination to 7 community hospitals., Methods: Participants included asthmatics 2 to 17 years admitted at 8 hospitals between 2003 and 2013. The EB-CPM was implemented at PCH between January 2008 and March 2009, then disseminated to 7 community hospitals between January and June 2011. We measured compliance using a composite score (CS) for 8 quality measures. Outcomes were compared between preimplementation and postimplementation periods. Confounding was addressed through multivariable regression analyses., Results: At PCH, the CS increased and remained at >90% for 5 years after implementation. We observed sustained reductions in asthma readmissions (P = .026) and length of stay (P < .001), a trend toward reduced costs (P = .094), and no change in hospital resource use, ICU transfers, or deaths. The CS also increased at the 7 community hospitals, reaching 80% to 90% and persisting >2 years after dissemination, with a slight but not significant readmission reduction (P = .119), a significant reduction in length of stay (P < .001) and cost (P = .053), a slight increase in hospital resource use (P = .032), and no change in ICU transfers or deaths., Conclusions: Our intervention resulted in sustained, long-term improvement in asthma care and outcomes at the tertiary care hospital and successful dissemination to community hospitals., (Copyright © 2015 by the American Academy of Pediatrics.)

Objective: To document the strengths and challenges of using electronic health records (EHRs) for comparative effectiveness research (CER)., Methods: A replicated case study of comparative effectiveness in hypertension treatment was conducted across 4 health systems, with instructions to extract data and document problems encountered using a specified list of required data elements. Researchers at each health system documented successes and challenges, and suggested solutions for addressing challenges., Results: Data challenges fell into 5 categories: missing data, erroneous data, uninterpretable data, inconsistencies among providers and over time, and data stored in noncoded text notes. Suggested strategies to address these issues include data validation steps, use of surrogate markers, natural language processing, and statistical techniques., Discussion: A number of EHR issues can hamper the extraction of valid data for cross-health system comparative effectiveness studies. Our case example cautions against a blind reliance on EHR data as a single definitive data source. Nevertheless, EHR data are superior to administrative or claims data alone, and are cheaper and timelier than clinical trials or manual chart reviews. All 4 participating health systems are pursuing pathways to more effectively use EHR data for CER.A partnership between clinicians, researchers, and information technology specialists is encouraged as a way to capitalize on the wealth of information contained in the EHR. Future developments in both technology and care delivery hold promise for improvement in the ability to use EHR data for CER.

Objective: Febrile infants in the first 90 days may have life-threatening serious bacterial infection (SBI). Well-appearing febrile infants with SBI cannot be distinguished from those without by examination alone. Variation in care resulting in both undertreatment and overtreatment is common., Methods: We developed and implemented an evidence-based care process model (EB-CPM) for the management of well-appearing febrile infants in the Intermountain Healthcare System. We report an observational study describing changes in (1) care delivery, (2) outcomes of febrile infants, and (3) costs before and after implementation of the EB-CPM in a children's hospital and in regional medical centers., Results: From 2004 through 2009, 8044 infants had 8431 febrile episodes, resulting in medical evaluation. After implementation of the EB-CPM in 2008, infants in all facilities were more likely to receive evidence-based care including appropriate diagnostic testing, determination of risk for SBI, antibiotic selection, decreased antibiotic duration, and shorter hospital stays (P < .001 for all). In addition, more infants had a definitive diagnosis of urinary tract infection or viral illness (P < .001 for both). Infant outcomes improved with more admitted infants positive for SBI (P = .011), and infants at low risk for SBI were more often managed without antibiotics (P < .001). Although hospital admissions were shortened by 27%, there were no cases of missed SBI. Health Care costs were also reduced, with the mean cost per admitted infant decreasing from $7178 in 2007 to $5979 in 2009 (-17%, P < .001)., Conclusions: The EB-CPM increased evidence-based care in all facilities. Infant outcomes improved and costs were reduced, substantially improving value.

Background: The trigger tool methodology uses clinical algorithms applied electronically to 'flag' medical records where adverse events (AEs) have most likely occurred. The authors sought to create surgical triggers to detect AEs in the ambulatory care setting., Methods: Four consecutive steps were used to develop ambulatory surgery triggers. First, the authors conducted a comprehensive literature review for surgical triggers. Second, a series of multidisciplinary focus groups (physicians, nurses, pharmacists and information technology specialists) provided user input on trigger selection. Third, a clinical advisory panel designed an initial set of 10 triggers. Finally, a three-phase Delphi process (surgical and trigger tool experts) evaluated and rated the suggested triggers., Results: The authors designed an initial set of 10 surgical triggers including five global triggers (flagging medical records for the suspicion of any AE) and five AE-specific triggers (flagging medical records for the suspicion of specific AEs). Based on the Delphi rating of the trigger's utility for system-level interventions, the final triggers were: (1) emergency room visit(s) within 21 days from surgery; (2) unscheduled readmission within 30 days from surgery; (3) unscheduled procedure (interventional radiological, urological, dental, cardiac or gastroenterological) or reoperation within 30 days from surgery; (4) unplanned initial hospital length of stay more than 24 h; and (5) lower-extremity Doppler ultrasound order entry and ICD code for deep vein thrombosis or pulmonary embolus within 30 days from surgery., Conclusion: The authors therefore propose a systematic methodology to develop trigger tools that takes into consideration previously published work, end-user preferences and expert opinion.

Introduction: During an infectious disease outbreak, the ability of a hospital to continue routine operations depends upon its ability to absorb expected losses in revenue when the routine charge base is replaced by infectious disease-related charges., Objective: The purpose of this study was to determine the probable financial impact of a bioterrorism event or an infectious disease outbreak on an academic and a community hospital., Methods: During the fiscal year 01 July 2002-30 June 2003, the average number of inpatient charges identified by the diagnosis-related-groups (DRGs) of an academic, tertiary care, Level-1 trauma center (PUH) and a community hospital (StM) were obtained retrospectively. Per diem charges were determined for patients with: (1) gastroenteritis; (2) sepsis; (3) meningitis; (4) tuberculosis (TB); and (5) pneumonia. These charges were used to simulate the financial coding of patients exposed to biological agents., Results: The total average PUH per diem charges per patient for all 31,530 discharges was (US)$10,516. Specifically, the average changes were $20,499 for patients with transplants, $14,406 for receiving critical care services, $12,650 for the provision of cardiac care, $11,576 for trauma/orthopedic care, and $8,259 for services for patients who suffered a stroke. For patients with infectious diseases, the average per diem charges per patient were: (1) $6,184 for patients with gastroenteritis; (2) $7,842 for patients with sepsis; (3) $10,831 for patients with meningitis; (4) $6,118 for patients with TB; and (5) $4,586 for patients with pneumonia. Per patient per day, PUH would generate a potential net on average loss of: (1) $4,332 for gastroenteritis; (2) $2,674 for sepsis; (3) $4,398 for TB; and (4) $5,930 for pneumonia replaced an admission. Patients with meningitis on average generated a net gain ($315) compared to the average, but would not compensate for the denial of transplant, cardiac, trauma/orthopedic, and some critical care services during the event. Total average StM per diem charges per patient for all 10,470 discharges equaled $3,008. Specifically, $4,965 for critical care, $3,022 for cardiac care, $4,397 for trauma/orthopedic care, and $3,037 for stroke services. For infectious diseases, the average per diem charge per patient was: (1) $2,273 (+$735) for gastroenteritis; (2) $3,047 (+$39) for sepsis; (3) $2,504 (-$504) for meningitis; (4) $2,887 ($120) for TB; and (5) $2,652 (-$356) for pneumonia (net loss/gain in parenthesis)., Conclusions: Through DRG analysis, the probable financial impact of a bioterrorist attack on a Health Care Delivery System is largely detrimental. Preparedness for a biological event must include an assessment of hospital capability and capacity to handle these types of patients, but also must consider the financial ability to absorb expected losses in charges or ways in which to recover the losses.

Background: Studies have established that physiologic instability and services mismatching precede adverse events in hospitalized patients. In response to these considerations, the concept of a Rapid Response System (RRS) has emerged. The responding team is commonly known as a medical emergency team (MET), rapid response team (RRT), or critical care outreach (CCO). Studies show that an RRS may improve outcome, but questions remain regarding the benefit, design elements, and advisability of implementing a MET system., Methods: In June 2005 an International Conference on Medical Emergency Teams (ICMET) included experts in patient safety, hospital medicine, critical care medicine, and METs. Seven of 25 had no experience with an RRS, and the remainder had experience with one of the three major forms of RRS. After preconference telephone and e-mail conversations by the panelists in which questions to be discussed were characterized, literature reviewed, and preliminary answers created, the panelists convened for 2 days to create a consensus document. Four major content areas were addressed: What is a MET response? Is there a MET syndrome? What are barriers to METS? How should outcome be measured? Panelists considered whether all hospitals should implement an RRS., Results: Patients needing an RRS intervention are suddenly critically ill and have a mismatch of resources to needs. Hospitals should implement an RRS, which consists of four elements: an afferent, "crisis detection" and "response triggering" mechanism; an efferent, predetermined rapid response team; a governance/administrative structure to supply and organize resources; and a mechanism to evaluate crisis antecedents and promote hospital process improvement to prevent future events.

Morgan provides an action plan for safer Canadian healthcare and argues that needed, fundamentally safer healthcare requires commitment to change from policymakers, healthcare leaders and practitioners. He posits that safer healthcare in Canada can be achieved through information technology (IT) and a national patient safety investigative agency.

Traditionally, medical education, research, and practice have focused on the care of the individual but an increasing emphasis on the care of populations has raised awareness among academic medical centers, integrated delivery systems, and managed care organizations of the value of embracing population-based health principles. Five principles are relevant in this regard: a community perspective, a clinical epidemiology perspective, evidence-based practice, an emphasis on outcomes, and an emphasis on prevention. This article describes these interrelated concepts together with specific strategies to effect implementation. Widespread awareness and adoption of these principles will have a profound impact on medical and public health education, practice, and ultimately the public's health.

Clinical process innovations (CPI) are central to the ability of organizations to negotiate the challenges of cost containment and quality improvement, yet many CPI have not met expectations. Perhaps most alarming is that the dissemination and implementation of CPI is not well understood. This is the second of two articles addressing the dissemination and use of CPI in integrated delivery systems. This article discusses those factors that have been identified as either facilitating or impeding the various stages in implementing CPI and suggests some intervention strategies to enhance opportunities for continuous CPI. Identifying the process and the factors driving the implementation of CPI is only part of the challenge. The development of CPI adequate to fully meet current challenges will require managers to re-examine existing paradigms and values influencing their actions to date. Within this context, the necessary staging of the innovation process within the life cycle, developing partnerships both within and outside the organization to gather the necessary resources and support, and multidimensional performance monitoring and feedback can prepare organizations and managers to better face the reality of managing the innovation process.

The changing healthcare environment has created a sense of urgency for continuous innovation in clinical care processes. Managers and clinicians are investing unprecedented funds and energy in the development of various clinical process innovations (CPI) such as clinical pathways, electronic workstations, and various forms of information technology. While increasing attention has been paid to the development of such initiatives, our understanding of how best to disseminate and ensure their use is limited. In this first of two articles dealing with the dissemination and use of CPI in integrated delivery systems, we present a "life cycle" model of the dissemination process and suggest opportunities for managing CPI. The management of CPI requires more than just an understanding of the factors that may facilitate or impede its implementation and use. Managers require an understanding of the actual process so that they can assess the specific implementation stage at which the organization is presently operating, and design appropriate interventions that can affect the process. A future article will identify the factors that facilitate and inhibit the process and suggest some intervention strategies.

Rural-urban comparisons have identified higher age-, race-, and sex-adjusted cancer incidence and mortality rates in urban populations for most anatomic sites, suggesting that rural populations are at lower risk from cancer. Conversely, findings that rural cancer patients are diagnosed at later stages of disease, that higher proportions of rural cancer cases are unstaged at diagnosis, and that rural cancer patients are at a more advanced stage of illness when referred to home health care agencies, suggest that rural cancer patients are disadvantaged when compared to their urban counterparts. This paper summarizes rural-urban patterns of cancer mortality, incidence, and survivorship since 1950; outlines rural-urban differences in utilization of health care services; questions the appropriateness of using rural-urban comparisons of cancer mortality and incidence to evaluate access to cancer care; and suggests potential approaches to the question of whether rural residents have access to cancer care comparable to that available to urban residents.