Biomedical subjects

Eugene C Nelson

Publications and source records attributed to Eugene C Nelson.

15 recordsLinked to original sources

PubMed2005

Publicly reporting comprehensive quality and cost data: a health care system's transparency initiative.

BACKGROUND: Transparency in health care, including the public reporting of health care results, is an expanding and unstoppable phenomenon. Health care systems have an opportunity to: (1) be proactive and accountable for the care they provide, (2) help patients learn more about their condition as a supplement to understanding the performance measures, and (3) use public reporting to foster process of care and outcome improvement initiatives. An overview is provided of the first 22 months of a transparency initiative at Dartmouth-Hitchcock Medical Center (DHMC). LAUNCHING THE TRANSPARENCY INITIATIVE: An interdisciplinary operations group works with the various clinical programs--both providers and patients--to identify what quality and cost measures are most desired by patients and what measures are the focus of the clinical program's internal measurement and reporting processes. The measures are presented on the DHMC Web site, with access to additional resources, such as clinical decision aids. DISCUSSION: A variety of factors are important to the transparency initiative--senior leaders' perceptions, risk management issues, resources required for the design and maintenance of the initiative, and developing both methodological protocols and technical systems.

Benchmarking↗

PubMed2004

How many patients are needed to provide reliable evaluations of individual clinicians?

PURPOSE: The purpose of this study was to determine how many patients are needed to provide reliable patient ratings of care at the individual clinician level. SETTING AND SOURCES OF DATA: The study was conducted in an academic medical center and was based on analysis of 34,985 patients who completed a 50-item survey rating the care received during a recent outpatient visit to a physician or midlevel provider. STUDY DESIGN: Analyses of patient satisfaction surveys was done to: 1) confirm the dimensions of satisfaction with outpatient care in an existing measure, and 2) determine the number of patients required to provide reliable estimates of clinician care for single items and an 11-item composite scale. PRINCIPAL FINDINGS: Factor analysis showed that the survey measured 2 dimensions of satisfaction: 1) clinician care, and 2) features of visiting the office. The 11-item clinician care scale had high reliability (Cronbach's alpha=0.97). The number of patients needed to achieve reliability of 0.80 at the clinician level was 66 for the 11-item scale and ranged from 52 to 91 for individual items. For primary care physicians only, the comparable number of patients per clinician was 77 for the 11-item scale and ranged from 50 to 147 across items. CONCLUSIONS: For the survey items that we analyzed, the answer to the question "How many patients are needed to obtain useful and reliable feedback?" is at least 50, but varies by item type (global vs. specific) and by number of items (composite scale or single-item rating) and by the conditions of use (for self-assessment and learning or reward and punishment).

Academic Medical Centers↗

PubMed2003

Patient characteristics can predict improvement in functional health after elective coronary artery bypass grafting.

BACKGROUND: Despite many patients undergoing coronary artery bypass grafting (CABG) to improve their functional status, literature in this area is limited. The purpose of this study is to determine the effect of CABG on the functional health of an elective population and to identify preoperative patient characteristics associated with improved functional health after surgery. METHODS: Physical and mental functional health was assessed before and 6 months after surgery with the Short-Form Health Survey (SF-36) in 1,061 consecutive patients undergoing elective, isolated CABG. Survey data were complete in 529 patients (49.9%). Preoperative information on patient demographics, severity of cardiovascular illness, and disease comorbidities was also prospectively collected. RESULTS: Six months post-CABG the mean summary score for physical function improved by 31.9% over baseline (45.1 versus 34.2, p < 0.0001). The mean summary score for mental function improved by 7.3% over baseline (51.3 versus 47.8, p < 0.0001). Overall 73.2% of patients showed improvement in physical function and 41.6% showed improvement in mental function. Multivariate logistic regression identified certain preoperative characteristics as negative correlates of a significant improvement in physical functioning: body mass index 35 kg/m2 or greater, diabetes with sequelae, chronic obstructive pulmonary disease, peripheral vascular disease, and baseline physical function. Baseline mental function and chronic obstructive pulmonary disease were identified as negative correlates and older age as a positive correlate of significant improvement in mental functioning. CONCLUSIONS: Patient characteristics exist that impact functional health after elective CABG. Knowledge of these characteristics may be helpful when counseling patients about expected improvement in functional health with CABG.

Activities of Daily Living↗

PubMed2003

Microsystems in health care: Part 2. Creating a rich information environment.

BACKGROUND: A rich information environment supports the functioning of the small, functional, frontline units--the microsystems--that provide most health care to most people. Three settings represent case examples of how clinical microsystems use data in everyday practice to provide high-quality and cost-effective care. CASES: At The Spine Center at Dartmouth, Lebanon, New Hampshire, a patient value compass, a one-page health status report, is used to determine if the provided care and services are meeting the patient's needs. In Summit, New Jersey, Overlook Hospital's emergency department (ED) uses uses real-time process monitoring on patient care cycle times, quality and productivity indicator tracking, and patient and customer satisfaction tracking. These data streams create an information pool that is actively used in this ED icrosystem--minute by minute, hourly, daily, weekly, and annually--to analyze performance patterns and spot flaws that require action. The Shock Trauma Intensive Care Unit (STRICU), Intermountain Health Care, Salt Lake City, uses a data system to monitor the "wired" patient remotely and share information at any time in real time. Staff can complete shift reports in 10 minutes. DISCUSSION: Information exchange is the interface that connects staff to patients and staff to staff within the microsystem; microsystem to microsystem; and microsystem to macro-organization.

Database Management Systems↗

PubMed2003

Microsystems in health care: Part 3. Planning patient-centered services.

BACKGROUND: Strategic focus on the clinical microsystems--the small, functional, frontline units that provide most health care to most people--is essential to designing the most efficient, population-based services. The starting place for designing or redesigning of clinical microsystems is to evaluate the four P's: the patient subpopulations that are served by the microsystem, the people who work together in the microsystem, the processes the microsystem uses to provide services, and the patterns that characterize the microsystem's functioning. GETTING STARTED: DIAGNOSING AND TREATING A CLINICAL MICROSYSTEM: Methods and tools have been developed for microsystem leaders and staff to use to evaluate the four P's--to assess their microsystem and design tests of change for improvement and innovation. PUTTING IT ALL TOGETHER: Based on its assessment--or diagnosis--a microsystem can help itself improve the things that need to be done better. Planning services is designed to decrease unnecessary variation, facilitate informed decision making, promote efficiency by continuously removing waste and rework, create processes and systems that support staff, and design smooth, effective, and safe patient care services that lead to measurably improved patient outcomes. CONCLUSION: The design of services leads to critical analysis of the resources needed for the right person to deliver the right care, in the right way, at the right time.

Efficiency, Organizational↗

PubMed2003

Microsystems in health care: Part 4. Planning patient-centered care.

BACKGROUND: Clinical microsystems are the essential building blocks of all health systems. At the heart of an effective microsystem is a productive interaction between an informed, activated patient and a prepared, proactive practice staff. Support, which increases the patient's ability for self-management, is an essential result of a productive interaction. This series on high-performing clinical microsystems is based on interviews and site visits to 20 clinical microsystems in the United States. This fourth article in the series describes how high-performing microsystems design and plan patient-centered care. PLANNING PATIENT-CENTERED CARE: Well-planned, patient-centered care results in improved practice efficiency and better patient outcomes. However, planning this care is not an easy task. Excellent planned care requires that the microsystem have services that match what really matters to a patient and family and protected time to reflect and plan. Patient self-management support, clinical decision support, delivery system design, and clinical information systems must be planned to be effective, timely, and efficient for each individual patient and for all patients. CONCLUSION: Excellent planned services and planned care are attainable today in microsystems that understand what really matters to a patient and family and have the capacity to provide services to meet the patient's needs.

Ambulatory Care Information Systems↗

PubMed2003

Microsystems in health care: Part 5. How leaders are leading.

BACKGROUND: Leading and leadership by formal and informal leaders goes on at all levels of microsystems--the essential building blocks of all health systems--and between them. It goes on between microsystems and other levels of the systems in health care. This series on high-performing clinical microsystems is based on interviews and site visits to 20 clinical microsystems in the United States. This fifth article in the series describes how leaders contribute to the performance of those microsystems. ANALYSIS OF INTERVIEWS: Interviews of leaders and staff members offer a rich understanding of the three core processes of leading. Building knowledge requires many behaviors of leaders and has many manifestations as leaders seek to build knowledge about the structure, processes, and patterns of work in their clinical microsystems. Taking action covers many different behaviors--making things happen, executing plans, making good on intentions. It focuses action on the way people are hired and developed and involves the way the work gets done. Reviewing and reflecting provides insight as to how the microsystem's patterns, processes, and structure enable the desired work to get done; what success looks like; and what will be next after that "success" is created. CONCLUSION: The focus on the processes of leading is intended to enable more people to develop into leaders and more people to share the roles of leading.

Canada↗

PubMed2003

Microsystems in health care: Part 6. Designing patient safety into the microsystem.

BACKGROUND: This article explores patient safety from a microsystems perspective and from an injury epidemiological perspective and shows how to embed safety into a microsystem's operations. MICROSYSTEMS PATIENT SAFETY SCENARIO: Allison, a 5-year-old preschooler with a history of "wheezy colds," and her mother interacted with several microsystems as they navigated the health care system. At various points, the system failed to address Allison's needs. The Haddon matrix provides a useful framework for analyzing medical failures in patient safety, setting the stage for developing countermeasures. CASE STUDY: The case study shows the types of failures that can occur in complex medical care settings such as those associated with pediatric procedural sedation. Six patient safety principles, such as "design systems to identify, prevent, absorb, and mitigate errors," can be applied in a clinical setting. In response to this particular case, its subsequent analysis, and the application of microsystems thinking, the anesthesiology department of the Children's Hospital at Dartmouth developed the PainFree Program to provide optimal safety for sedated patients. CONCLUSION: Safety is a property of a microsystem and it can be achieved only through thoughtful and systematic application of a broad array of process, equipment, organization, supervision, training, simulation, and team-work changes.

Academic Medical Centers↗

PubMed2003

Microsystems in health care: Part 8. Developing people and improving work life: what front-line staff told us.

BACKGROUND: The articles in the Microsystems in Health Care series have focused on the success characteristics of high-performing clinical microsystems. Realization is growing about the importance of attracting, selecting, developing, and engaging staff. By optimizing the work of all staff members and by promoting a culture where everyone matters, the microsystem can attain levels of performance not previously experienced. CASE STUDY: At Massachusetts General Hospital Downtown Associates (Boston), a primary care practice, the human resource processes are specified and predictable, from a candidate's initial contact through each staff member's orientation, performance management, and professional development. Early on, the new employee receives materials about the practice, including a practice overview, his or her typical responsibilities, the performance evaluation program, and continuous quality improvement. Ongoing training and education are supported with skill labs, special education nights, and cross-training. The performance evaluation program, used to evaluate the performance of all employees, is completed during the 90-day orientation and training, quarterly for one year, and annually. CONCLUSION: Some health care settings enjoy high morale, high quality, and high productivity, but all too often this is not the case. The case study offers an example of a microsystem that has motivated its staff and created a positive and dynamic workplace.

Boston↗

PubMed2003

Microsystems in health care: Part 9. Developing small clinical units to attain peak performance.

BACKGROUND: This last Microsystems in Health Care series article focuses on what it takes, in the short term and long term, for clinical microsystems--the small, functional, front-line units that provide the most health care to the most people--to attain peak performance. CASE STUDY: A case study featuring the intensive care nursery at Dartmouth-Hitchcock Medical Center illustrates the 10-year evolution of a clinical microsystem. Related evolutionary principles begin with the intention to excel, involve all the players, use measurement and feedback, and create a learning system. DISCUSSION: A microsystem's typical developmental journey toward excellence entails five stages of growth--awareness as an interdependent group with the capacity to make changes, connecting routine daily work to the high purpose of benefiting patients, responding successfully to strategic challenges, measuring the microsystem's performance as a system, and juggling improvements while taking care of patients. A MODEL CURRICULUM: Health system leaders can sponsor an action-learning program to catalyze development of clinical microsystems. A "green-belt curriculum" can help clinical staff members acquire the fundamental knowledge and skills that they will need to master if they are to increase their capacity to attain higher levels of performance; uses action-learning theory and sound education principles to provide the opportunity to learn, test, and gain some degree of mastery; and involves people in the challenging real work of improving.

Benchmarking↗

PubMed2002

Promoting quality improvement research.

The authors show how an internal grant program can stimulate quality improvement research by providing technical and financial support to clinicians and employees.

Academic Medical Centers↗

PubMed2002

Microsystems in health care: Part 1. Learning from high-performing front-line clinical units.

BACKGROUND: Clinical microsystems are the small, functional, front-line units that provide most health care to most people. They are the essential building blocks of larger organizations and of the health system. They are the place where patients and providers meet. The quality and value of care produced by a large health system can be no better than the services generated by the small systems of which it is composed. METHODS: A wide net was cast to identify and study a sampling of the best-quality, best-value small clinical units in North America. Twenty microsystems, representing different component parts of the health system, were examined from December 2000 through June 2001, using qualitative methods supplemented by medical record and finance reviews. RESULTS: The study of the 20 high-performing sites generated many best practice ideas (processes and methods) that microsystems use to accomplish their goals. Nine success characteristics were related to high performance: leadership, culture, macro-organizational support of microsystems, patient focus, staff focus, interdependence of care team, information and information technology, process improvement, and performance patterns. These success factors were interrelated and together contributed to the microsystem's ability to provide superior, cost-effective care and at the same time create a positive and attractive working environment. CONCLUSIONS: A seamless, patient-centered, high-quality, safe, and efficient health system cannot be realized without the transformation of the essential building blocks that combine to form the care continuum.

Attitude of Health Personnel↗

PubMedDate not supplied

Good measurement for good improvement work.

PURPOSE: To provide guidance on using measurement to support the conduct of local quality improvement projects that will strengthen the evaluation of results and increase their potential for publication. TARGET GROUP: Individuals leading quality improvement efforts who wish to enhance their use of measurement. PROCEDURES TO PROMOTE GOOD MEASUREMENT: Eleven procedures are offered to promote intelligent measurement in quality improvement research that may become publishable: 1. Start with an important topic 2. Develop a clear aim statement 3. Turn the aim statement into key questions 4. Develop a theory about causes and effects, process changes and predictable sources of variation 5. Construct a research design and accompanying dummy data displays to answer your primary research questions 6. Develop and use operational definitions for each variable needed to make your dummy data displays 7. Design a data collection plan to gather information on each variable that will enable you to generate reliable, valid, and sensitive measures related to each research question 8. Pilot test the data collection plan, construct preliminary data displays, and revise your methods based on what you learn 9. Stay close to the data collection process as the data plan goes from idea to execution 10. Perform data analysis and display results in a way that answers your key questions. 11. Review and document the strengths and limitations of your measurement work and use this knowledge to guide intelligent interpretation of the observed results.

Health Facilities↗

PubMedDate not supplied

Guidelines for appraisal and publication of PDSA quality improvement.

Plan-do-study-act (PDSA) quality improvement is the application of the scientific method to implement and test the effects of change ideas on the performance of the health care system. Users of quality improvement could benefit with markers to gauge the "best" science. Four core questions can determine the value of a quality improvement study: Is the quality improvement study pertinent and relevant? Are the results valid? Are appropriate criteria used to interpret the results? Will the study help you with your practice or organization of care? A set of guidelines is provided to help answer these questions. Similar guidelines exist for randomized clinical trials and clinical-epidemiologic observational studies. Analogous to these existing research guidelines, the PDSA quality improvement guidelines will provide researchers and reviewers with succinct standards of methodological rigor to assist in critical appraisal of quality improvement protocols and publications.

Guidelines as Topic↗

PubMedDate not supplied

Quality improvement learning collaboratives.

PURPOSE: To identify and synthesize characteristics of successful data-driven Quality improvement learning collaboratives (QILCs) in the United States and Europe, and to extend previously discussed and newly identified guidelines for developing successful data-driven QILCs across health care settings and systems. METHODS: An interview guide of open-ended questions was developed and posed to 18 key informants of various disciplines involved in the development and implementation of successful QILCs across 10 organizations in 3 countries. Aspects of successful QILCs were analyzed to identify patterns emerging from structure-process interactions between complex health care systems. RESULTS: Shared patterns of successful collaboratives included cultivating trust, attendance to the human dimension, nonlinear development, attendance to organizational culture, integrated philosophy of quality improvement, and a focus on process and outcome measurement to drive change. This study extends the knowledge base through synthesis of findings from previous quality improvement research with the findings from this study to develop guidelines for establishing and developing successful QILCs. CONCLUSIONS: The core characteristics identified in this study were critical to successful collaboration when these approaches were used in the contexts identified. The intrinsic complexity of QILCs requires that effectiveness studies employ qualitative as well as quantitative methodologies.

Cooperative Behavior↗