Real time patient safety audits: improving safety every day.

BACKGROUND: Timely error detection including feedback to clinical staff is a prerequisite for focused improvement in patient safety. Real time auditing, the efficacy of which has been repeatedly demonstrated in industry, has not been used previously to evaluate patient safety. Methods successful at improving quality and safety in industry may provide avenues for improvement in patient safety. OBJECTIVE: Pilot study to determine the feasibility and utility of real time safety auditing during routine clinical work in an intensive care unit (ICU). METHODS: A 36 item patient safety checklist was developed via a modified Delphi technique. The checklist focused on errors associated with delays in care, equipment failure, diagnostic studies, information transfer and non-compliance with hospital policy. Safety audits were performed using the checklist during and after morning work rounds thrice weekly during the 5 week study period from January to March 2003. RESULTS: A total of 338 errors were detected; 27 (75%) of the 36 items on the checklist detected >or=1 error. Diverse error types were found including unlabeled medication at the bedside (n = 31), ID band missing or in an inappropriate location (n = 70), inappropriate pulse oximeter alarm setting (n = 22), and delay in communication/information transfer that led to a delay in appropriate care (n = 4). CONCLUSIONS: Real time safety audits performed during routine work can detect a broad range of errors. Significant safety problems were detected promptly, leading to rapid changes in policy and practice. Staff acceptance was facilitated by fostering a blame free "culture of patient safety" involving clinical personnel in detection of remediable gaps in performance, and limiting the burden of data collection.

Effect of a scoring system and protocol for sedation on duration of patients' need for ventilator support in a surgical intensive care unit.

PROBLEM: Need for improved sedation strategy for adults receiving ventilator support. DESIGN: Observational study of effect of introduction of guidelines to improve the doctors' and nurses' performance. The project was a prospective improvement and was part of a national quality improvement collaborative. BACKGROUND AND SETTING: A general mixed surgical intensive care unit in a university hospital; all doctors and nurses in the unit; all adult patients (>18 years) treated by intermittent positive pressure ventilation for more than 24 hours. KEY MEASURES FOR IMPROVEMENT: Reduction in patients' mean time on a ventilator and length of stay in intensive care over a period of 11 months; anonymous reporting of critical incidents; staff perceptions of ease and of consequences of changes. STRATEGIES FOR CHANGE: Multiple measures (protocol development, educational presentations, written guidelines, posters, flyers, emails, personal discussions, and continuous feedback) were tested, rapidly assessed, and adopted if beneficial. EFFECTS OF CHANGE: Mean ventilator time decreased by 2.1 days (95% confidence interval 0.7 to 3.6 days) from 7.4 days before intervention to 5.3 days after. Mean stay decreased by 1.0 day (-0.9 to 2.9 days) from 9.3 days to 8.3 days. No accidental extubations or other incidents were identified. LESSONS LEARNT: Relatively simple changes in sedation practice had significant effects on length of ventilator support. The change process was well received by the staff and increased their interest in identifying other areas for improvement.

Statistical process control as a tool for research and healthcare improvement.

Improvement of health care requires making changes in processes of care and service delivery. Although process performance is measured to determine if these changes are having the desired beneficial effects, this analysis is complicated by the existence of natural variation-that is, repeated measurements naturally yield different values and, even if nothing was done, a subsequent measurement might seem to indicate a better or worse performance. Traditional statistical analysis methods account for natural variation but require aggregation of measurements over time, which can delay decision making. Statistical process control (SPC) is a branch of statistics that combines rigorous time series analysis methods with graphical presentation of data, often yielding insights into the data more quickly and in a way more understandable to lay decision makers. SPC and its primary tool-the control chart-provide researchers and practitioners with a method of better understanding and communicating data from healthcare improvement efforts. This paper provides an overview of SPC and several practical examples of the healthcare applications of control charts.

Quality collaboratives: lessons from research.

Quality improvement collaboratives are increasingly being used in many countries to achieve rapid improvements in health care. However, there is little independent evidence that they are more cost effective than other methods, and little knowledge about how they could be made more effective. A number of systematic evaluations are being performed by researchers in North America, the UK, and Sweden. This paper presents the shared ideas from two meetings of these researchers. The evidence to date is that some collaboratives have stimulated improvements in patient care and organisational performance, but there are significant differences between collaboratives and teams. The researchers agreed on the possible reasons why some were less successful than others, and identified 10 challenges which organisers and teams need to address to achieve improvement. In the absence of more conclusive evidence, these guidelines are likely to be useful for collaborative organisers, teams and their managers and may also contribute to further research into collaboratives and the spread of innovations in health care.

Collaborative quality improvement for neonatal intensive care. NIC/Q Project Investigators of the Vermont Oxford Network.

OBJECTIVE: To make measurable improvements in the quality and cost of neonatal intensive care using a multidisciplinary collaborative quality improvement model. DESIGN: Interventional study. Patient demographic and clinical information for infants with birth weight 501 to 1500 g was collected using the Vermont Oxford Network Database for January 1, 1994 to December 31, 1997. SETTING: Ten self-selected neonatal intensive care units (NICUs) received the intervention. They formed 2 subgroups (6 NICUs working on infection, 4 NICUs working on chronic lung disease). Sixty-six other NICUs served as a contemporaneous comparison group. PATIENTS: Infants with birth weight 501 to 1500 g born at or admitted within 28 days of birth between 1994 and 1997 to the 6 study NICUs in the infection group (n = 3063) and the 66 comparison NICUs (n = 21 509); infants with birth weight 501 to 1000 g at the 4 study NICUs in the chronic lung disease group (n = 738). INTERVENTIONS: NICUs formed multidisciplinary teams that worked together under the direction of a trained facilitator over a 3-year period beginning in January 1995. They received instruction in quality improvement, reviewed performance data, identified common improvement goals, and implemented "potentially better practices" developed through analysis of the processes of care, literature review, and site visits. MAIN OUTCOME MEASURES: The rates of infection after the third day of life with coagulase-negative staphylococcal or other bacterial pathogens for infants with birth weight 501 to 1500 g, and the rates of oxygen supplementation or death at 36 weeks' adjusted gestational age for infants with birth weight 501 to 1000 g. RESULTS: Between 1994 and 1996, the rate of infection with coagulase-negative staphylococcus decreased from 22.0% to 16.6% at the 6 project NICUs in the infection group; the rate of supplemental oxygen at 36 weeks' adjusted gestational age decreased from 43.5% to 31.5% at the 4 NICUs in the chronic lung disease group. There was heterogeneity in the effects among the NICUs in both project groups. The changes observed at the project NICUs for these outcomes were significantly larger than those observed at the 66 comparison NICUs over the 4-year period from 1994 to 1997. CONCLUSION: We conclude that multidisciplinary collaborative quality improvement has the potential to improve the outcomes of neonatal intensive care.

Economic implications of neonatal intensive care unit collaborative quality improvement.

OBJECTIVE: To make measurable improvements in the quality and cost of neonatal intensive care using a multidisciplinary collaborative quality improvement model. DESIGN: Interventional study. Data on treatment costs were collected for infants with birth weight 501 to 1500 g for the period of January 1, 1994 to December 31, 1997. Data on resources expended by hospitals to conduct this project were collected in a survey for the period January 1, 1995 to December 31, 1996. SETTING: Ten self-selected neonatal intensive care units (NICUs) received the intervention. They formed 2 subgroups (6 NICUs working on infection, 4 NICUs working on chronic lung disease). Nine other NICUs served as a contemporaneous comparison group. PATIENTS: Infants with birth weight 501 to 1500 g born at or admitted within 28 days of birth between 1994 and 1997 to the 6 study NICUs in the infection group (N = 2993) and the 9 comparison NICUs (N = 2203); infants with birth weight 501 to 1000 g at the 4 study NICUs in the chronic lung disease group (N = 663) and the 9 comparison NICUs (N = 1007). INTERVENTIONS: NICUs formed multidisciplinary teams which worked together to undertake a collaborative quality improvement effort between January 1995 and December 1996. They received instruction in quality improvement, reviewed performance data, identified common improvement goals, and implemented "potentially better practices" developed through analysis of the processes of care, literature review, and site visits. MAIN OUTCOME MEASURES: Treatment cost per infant is the primary economic outcome measure. In addition, the resources spent by hospitals in undertaking the collaborative quality improvement effort were determined. RESULTS: Between 1994 and 1996, the median treatment cost per infant with birth weight 501 to 1500 g at the 6 project NICUs in the infection group decreased from $57 606 to $46 674 (a statistical decline); at the 4 chronic lung disease hospitals, for infants with birth weights 501 to 1000 g, it decreased from $85 959 to $77 250. Treatment costs at hospitals in the control group rose over the same period. There was heterogeneity in the effects among the NICUs in both project groups. Cost savings were maintained in the year following the intervention. On average, hospitals spent $68 206 in resources to undertake the collaborative quality improvement effort between 1995 and 1996. Two thirds of these costs were incurred in the first year, with the remaining third in the second year. The average savings per hospital in patient care costs for very low birth weight infants in the infection group was $2.3 million in the post-intervention year (1996). There was considerable heterogeneity in the cost savings across hospitals associated with participation in the collaborative quality improvement project. CONCLUSION: Cost savings may be achieved as a result of collaborative quality improvement efforts and when they occur, they appear to be sustainable, at least in the short run. In high-cost patient populations, such as infants with very low birth weights, cost savings can quickly offset institutional expenditures for quality improvement efforts.

Quality improvement around the world: how much we can learn from each other.

The USA National Forum on Quality Improvement in Health Care--organised by the Institute of Healthcare Improvement (Boston USA)--attracts many people from outside North America. At the 1999 meeting 20 countries were represented. A session on "Quality improvement around the world" was included in the pre-conference programme to bring together people working in many countries to explore and compare their experiences in a programme of short presentations (table 1). This article draws together some of the themes that emerged from the presentations and from the discussion.

Paul E. Plsek on creativity and innovation in organizational change. Interview by Paul L. Green.

Mr. Plsek is an independent consultant with 20 years of diverse experience in the field of quality management. He has written numerous articles, presented papers at a variety of conferences, and led seminars for more than 10,000 managers and executives in diverse companies. His seminar, "Creative Thinking for Serious People," has taught people that there can be a serious approach to innovation. He also has served in a variety of engineering and management positions at AT&T and Bell Laboratories and is a senior fellow with the Institute for Healthcare Improvement. Mr. Plsek has BS and MS degrees in electrical engineering from Texas A & M University and the Polytechnic Institute of New York (Brooklyn).

Innovative thinking for the improvement of medical systems.

If health care systems are not delivering the desired results, those systems must be changed in some way. Innovative thinking is sometimes needed to generate ideas for improvement. Many persons erroneously believe that innovative thinking is a special gift or that it requires an air of lightheartedness that seems inappropriate in a health care setting. Current research in the cognitive sciences has yielded methods to help individual persons and groups generate innovative ideas. These methods do not require any special gift and can be practiced in a serious way. Through a case example from a health maintenance organization, this paper shows that, given some direction, groups of health care professionals can produce useful and innovative ideas. The tools of idea generation are based on three principles: mental attention, escape, and movement. Activities that help persons pay attention to their current situations in a different way, escape their current mental patterns about the situation, and maintain movement in their thoughts support efforts to generate innovative, testable ideas for health system improvements. This paper illustrates several methods of stimulating innovative thinking and shows the ways in which they can be applied in health care.

Quality improvement methods in clinical medicine.

This article surveys the methods and tools of quality improvement used today in health care. Specifically, we describe how clinicians can use these methods to impact the clinical practice of medicine. Improvement teams from a variety of health care organizations have reported the successful use of basic methods such as group work, flowcharting, data collection, and graphical data analysis. In addition to these incremental, problem-solving methods borrowed from the industrial practice of improvement, we have also seen the use of specific process design methods in health care applications such as care path development. The pace of change in health care has also led to the practical development of newer methods for rapid cycle improvement. We will review the basic approach behind these methods and illustrate key elements such as the ideas of change concepts and small-scale tests of change. Unfortunately, whereas these methods have been very successful and highly appealing to improvement practitioners, they may also have inadvertently widened a gulf between these practitioners and traditional health-services and clinical researchers. We offer an assessment of this issue and suggest ways to narrow the communication gap. Measurement has also traditionally been a part of the thinking about quality assurance and improvement in health care. We review the new philosophy of measurement that has emerged from recent improvement thinking and describe the use of control charts in clinical improvement. Benchmarking and multiorganizational collaboratives are more recent innovations in the ways we approach improvement in health care. These efforts go beyond simple measurement and explore the why and how associated with the widespread variation in performance in health care. We explore a variety of health care examples to illustrate these methods and the lessons learned in their use. We conclude the article with an overview of four habits that we believe are essential for health care organizations and individual clinicians to adopt to bring about real improvement in the clinical practice of medicine. These are the habits for: 1) viewing clinical practice as a process; 2) evidence-based practice; 3) collaborative learning; and 4) change.

From resistance to attraction: a different approach to change.

How can physician executives interested in promoting change be more effective at the task? This article explores how to create an attraction towards change, as opposed to viewing change as overcoming resistance. Learning to recognize naturally occurring change, identify attractors, explore the rationality of others' points of view, and reduce risk are clear and constructive insights from research and emerging systems science. Other ideas to consider are: Understand the issues of those you wish to change, create changes that are "exothermic," produce system changes, accept responsibility, learn from failed efforts, and focus on building relationships of trust.

Collaborating across organizational boundaries to improve the quality of care.

The paradigm of modern quality management is in wide use in health care. Although much of the initial effort in health care has focused on improving service, administrative, and support processes, many organizations are also using these concepts to improve clinical care. The analysis of data on clinical outcomes has undoubtedly led to many local improvements, but such analysis is inevitably limited by three issues: small samples, lack of detailed knowledge of what others are doing, and paradigm paralysis. These issues can be partially overcome when multiple health care organizations work together on focused clinical quality improvement efforts. Through the use of multiorganizational collaborative groups, literature reviews, expert panels, best-practice conferences, multiorganizational databases, and bench-marking groups, organizations can effectively pool data and learn from the many natural experiments constantly underway in the health care community. This article outlines the key concepts behind such collaborative improvement efforts and describes pioneering work in the application of these techniques in health care. A better understanding and wider use of collaborative improvement efforts may lead to dramatic breakthroughs in clinical outcomes in the coming years.

Interpreting quality improvement data with time-series analyses.

In quality improvement efforts, the data are frequently a series of measurements taken over time. A collection of statistical methods, commonly referred to as time-series analysis, provides a simple and understandable method for interpreting this longitudinal data. In this article, we present a time-series analysis of data on the quality of prenatal care at a mid-sized public hospital. We will demonstrate some simple tests that alert us to the potential value of using more sophisticated tests of association such as regression. Using regression, we show how to confirm a visual impression of an improvement. The analytical approach we present here is useful with many types of process or outcome data from health care quality improvement efforts.

Variability in clinical systems: applying modern quality control methods to health care.

BACKGROUND: Quality health care depends on timely completion of sequences of clinical care. This study evaluated the concepts and tools of quality management for measuring system performance in ambulatory care. STUDY DESIGN: Clinical staff in nine centers of a group model health maintenance organization described the procedure for ordering and completing a complete blood count, mammogram, and surgical consultation. Variability was noted among the processes as intended and as actually performed, as well as inconsistencies reported within and among centers. In two centers investigators tracked performance of key sequences of care and the achievement of other key quality characteristics believed desired by physicians and patients. Computerized patient records and departmental files were the only available sources of data for assessing completion and followup of tests and consultations. Even these data were difficult to obtain and, in many instances, incomplete. RESULTS: Although data were often difficult to obtain, the quality management techniques used were helpful in revealing process failures that appeared to be the result of design flaws built into the clinical systems. CONCLUSION: Robust process designs and improved management information systems for monitoring these processes are recommended to reduce variability and improve the quality of clinical care.

Techniques for managing quality.

The science of quality management is an eclectic collection of concepts and methods primarily borrowed from other fields. Techniques roughly fall into three categories involving quality improvement, planning, and measurement. Improvement techniques include models to guide team-based efforts, tools for process description, and tools for data analysis. These methods are the most visible artifacts of CQI efforts in health care organizations today. Less widely known, but equally powerful, are the techniques of quality planning. There are models to guide both process design and strategic planning, methods for identifying customer needs, and tools to support these efforts. Finally, while measurement is a traditionally well-developed area in health care, industrial quality management science broadens our outlook about what is important to measure. It also provides the technique of benchmarking, which suggests that we look beyond our own organization when we measure performance.

Tutorial: planning for data collection. Part I: Asking the right question.

While data collection is fundamental to any scientifically based effort, health care improvement teams often report that it is one of the most frustrating aspects of their work. Groups that do succeed in gathering some data can also sometimes find that the data they have worked so hard to obtain does not really help them or deepen their understanding of the process. The root of many of these problems can be traced to insufficient planning for data collection and to fundamental misunderstandings about the eventual goals of such studies. In this tutorial, we will explore a systematic way of thinking through the planning for data collection that will result in a higher probability of generating useful information.