Measuring a Safety Culture: Critical Pathway or Academic Activity?

he Institute of Medicine (IOM) identified six core needs in a health care system, the first of which was safety. 1 Furthermore, several IOM committees and others have identified the creation of a “culture of safety ” as the key institutional requirement to achieve safe medical care. 1–3 In this issue of the journal, Modak et al. 4 present an instrument that may help measure the extent to which a patient safety culture exists in an ambulatory setting. While these authors and others have done considerable work on defining and measuring a culture of safety in the hospital setting, 5,6 few have tackled the difficult task of measuring a safety culture in the ambulatory arena within the US health care system. Even in the hospital setting, where there has been more effort, the development of a culture of safety within all US hospitals has been spotty and, for some safety advocates, too slow. 7 There are many potential reasons for the poor progress in developing a culture of safety: confusion about the difference between safety and quality, concerns that increasing safety will further erode profits, or perhaps simply a lack of attention by institutional leaders. Whatever the reasons for the slow pace of transformation across the nation’s 5,000-plus hospitals, it is likely that this transformation will be even more difficult to achieve in the much larger and more diverse ambulatory setting. Thus, it is important to define and measure an ambulatory culture of safety. It is also difficult, perhaps impossible, to change beliefs, attitudes, knowledge, or actions (all components of a “culture”) without some form of feedback. Therefore, a necessary step in creating a culture of safety is to develop tools to measure the components of that culture. For those individuals and institutions that wish to truly improve the safety of the care they deliver, the creation and testing of tools such as the Safety Attitudes Questionnaire-Ambulatory (SAQ-A) version is critical. Beliefs, attitudes, and knowledge do not always lend themselves to clear-cut end points. Thus, we can expect to see more than one safety culture measuremen

Medical oncology patients' preferences with regard to health care: development of a patient-driven questionnaire

BACKGROUND: To improve quality of care for cancer patients, it is important to have an insight on the patient's view on health care and on their specific wishes, needs and preferences, without restriction and without influence of researchers and health care providers. The aim of this study was to develop a questionnaire assessing medical oncology patients' preferences for health care based on their own input. PATIENTS AND METHODS: Items were generated using 10 focus group interviews with 51 cancer patients. A preliminary questionnaire was handed out to 681 patients of seven Dutch departments of medical oncology. Explorative factor analysis was carried out on the 386 returned questionnaires (response 57%). RESULTS: Focus group interviews resulted in a preliminary questionnaire containing 136 items. Explorative factor analysis resulted in a definitive questionnaire containing 123 items (21 scales and eight single items). Patients rated expertise, safety, performance and attitude of physicians and nurses as the most important issues in cancer care. CONCLUSION: This questionnaire may be used to assess preferences of cancer patients and to come to a tailored approach of health care that meets patients' wishes and needs

A draft framework for measuring progress towards the development of a national health information infrastructure

BACKGROUND: American public policy makers recently established the goal of providing the majority of Americans with electronic health records by 2014. This will require a National Health Information Infrastructure (NHII) that is far more complete than the one that is currently in its formative stage of development. We describe a conceptual framework to help measure progress toward that goal. DISCUSSION: The NHII comprises a set of clusters, such as Regional Health Information Organizations (RHIOs), which, in turn, are composed of smaller clusters and nodes such as private physician practices, individual hospitals, and large academic medical centers. We assess progress in terms of the availability and use of information and communications technology and the resulting effectiveness of these implementations. These three attributes can be studied in a phased approach because the system must be available before it can be used, and it must be used to have an effect. As the NHII expands, it can become a tool for evaluating itself. SUMMARY: The NHII has the potential to transform health care in America – improving health care quality, reducing health care costs, preventing medical errors, improving administrative efficiencies, reducing paperwork, and increasing access to affordable health care. While the President has set an ambitious goal of assuring that most Americans have electronic health records within the next 10 years, a significant question remains "How will we know if we are making progress toward that goal?" Using the definitions for "nodes" and "clusters" developed in this article along with the resulting measurement framework, we believe that we can begin a discussion that will enable us to define and then begin making the kinds of measurements necessary to answer this important question

Quantitative data management in quality improvement collaboratives

<p>Abstract</p> <p>Background</p> <p>Collaborative approaches in quality improvement have been promoted since the introduction of the Breakthrough method. The effectiveness of this method is inconclusive and further independent evaluation of the method has been called for. For any evaluation to succeed, data collection on interventions performed within the collaborative and outcomes of those interventions is crucial. Getting enough data from Quality Improvement Collaboratives (QICs) for evaluation purposes, however, has proved to be difficult. This paper provides a retrospective analysis on the process of data management in a Dutch Quality Improvement Collaborative. From this analysis general failure and success factors are identified.</p> <p>Discussion</p> <p>This paper discusses complications and dilemma's observed in the set-up of data management for QICs. An overview is presented of signals that were picked up by the data management team. These signals were used to improve the strategies for data management during the program and have, as far as possible, been translated into practical solutions that have been successfully implemented.</p> <p>The recommendations coming from this study are:</p> <p>From our experience it is clear that quality improvement programs deviate from experimental research in many ways. It is not only impossible, but also undesirable to control processes and standardize data streams. QIC's need to be clear of data protocols that do not allow for change. It is therefore minimally important that when quantitative results are gathered, these results are accompanied by qualitative results that can be used to correctly interpret them.</p> <p>Monitoring and data acquisition interfere with routine. This makes a database collecting data in a QIC an intervention in itself. It is very important to be aware of this in reporting the results. Using existing databases when possible can overcome some of these problems but is often not possible given the change objective of QICs.</p> <p>Introducing a standardized spreadsheet to the teams is a very practical and helpful tool in collecting standardized data within a QIC. It is vital that the spreadsheets are handed out before baseline measurements start.</p

Progress along developmental tracks for electronic health records implementation in the United States

The development and implementation of electronic health records (EHR) have occurred slowly in the United States. To date, these approaches have, for the most part, followed four developmental tracks: (a) Enhancement of immunization registries and linkage with other health records to produce Child Health Profiles (CHP), (b) Regional Health Information Organization (RHIO) demonstration projects to link together patient medical records, (c) Insurance company projects linked to ICD-9 codes and patient records for cost-benefit assessments, and (d) Consortia of EHR developers collaborating to model systems requirements and standards for data linkage. Until recently, these separate efforts have been conducted in the very silos that they had intended to eliminate, and there is still considerable debate concerning health professionals access to as well as commitment to using EHR if these systems are provided. This paper will describe these four developmental tracks, patient rights and the legal environment for EHR, international comparisons, and future projections for EHR expansion across health networks in the United States

A group randomized trial of a complexity-based organizational intervention to improve risk factors for diabetes complications in primary care settings: study protocol

<p>Abstract</p> <p>Background</p> <p>Most patients with type 2 diabetes have suboptimal control of their glucose, blood pressure (BP), and lipids – three risk factors for diabetes complications. Although the chronic care model (CCM) provides a roadmap for improving these outcomes, developing theoretically sound implementation strategies that will work across diverse primary care settings has been challenging. One explanation for this difficulty may be that most strategies do not account for the complex adaptive system (CAS) characteristics of the primary care setting. A CAS is comprised of individuals who can learn, interconnect, self-organize, and interact with their environment in a way that demonstrates non-linear dynamic behavior. One implementation strategy that may be used to leverage these properties is practice facilitation (PF). PF creates time for learning and reflection by members of the team in each clinic, improves their communication, and promotes an individualized approach to implement a strategy to improve patient outcomes.</p> <p>Specific objectives</p> <p>The specific objectives of this protocol are to: evaluate the effectiveness and sustainability of PF to improve risk factor control in patients with type 2 diabetes across a variety of primary care settings; assess the implementation of the CCM in response to the intervention; examine the relationship between communication within the practice team and the implementation of the CCM; and determine the cost of the intervention both from the perspective of the organization conducting the PF intervention and from the perspective of the primary care practice.</p> <p>Intervention</p> <p>The study will be a group randomized trial conducted in 40 primary care clinics. Data will be collected on all clinics, with 60 patients in each clinic, using a multi-method assessment process at baseline, 12, and 24 months. The intervention, PF, will consist of a series of practice improvement team meetings led by trained facilitators over 12 months. Primary hypotheses will be tested with 12-month outcome data. Sustainability of the intervention will be tested using 24 month data. Insights gained will be included in a delayed intervention conducted in control practices and evaluated in a pre-post design.</p> <p>Primary and secondary outcomes</p> <p>To test hypotheses, the unit of randomization will be the clinic. The unit of analysis will be the repeated measure of each risk factor for each patient, nested within the clinic. The repeated measure of glycosylated hemoglobin A1c will be the primary outcome, with BP and Low Density Lipoprotein (LDL) cholesterol as secondary outcomes. To study change in risk factor level, a hierarchical or random effect model will be used to account for the nesting of repeated measurement of risk factor within patients and patients within clinics.</p> <p>This protocol follows the CONSORT guidelines and is registered per ICMJE guidelines:</p> <p>Clinical Trial Registration Number</p> <p>NCT00482768</p