The SQUIRE (Standards for QUality Improvement Reporting Excellence) guidelines for quality improvement reporting: explanation and elaboration.

As the science of quality improvement in health care advances, the importance of sharing its accomplishments through the published literature increases. Current reporting of improvement work in health care varies widely in both content and quality. It is against this backdrop that a group of stakeholders from a variety of disciplines has created the Standards for QUality Improvement Reporting Excellence, which we refer to as the SQUIRE publication guidelines or SQUIRE statement. The SQUIRE statement consists of a checklist of 19 items that authors need to consider when writing articles that describe formal studies of quality improvement. Most of the items in the checklist are common to all scientific reporting, but virtually all of them have been modified to reflect the unique nature of medical improvement work. This "Explanation and Elaboration" document (E & E) is a companion to the SQUIRE statement. For each item in the SQUIRE guidelines the E & E document provides one or two examples from the published improvement literature, followed by an analysis of the ways in which the example expresses the intent of the guideline item. As with the E & E documents created to accompany other biomedical publication guidelines, the purpose of the SQUIRE E & E document is to assist authors along the path from completion of a quality improvement project to its publication. The SQUIRE statement itself, this E & E document, and additional information about reporting improvement work can be found at http://www.squire-statement.org.

Using data to improve medical practice by measuring processes and outcomes of care.

BACKGROUND: The purpose of this article is to help clinicians expand their use of data to improve medical practice performance and to do improvement research. Clinical practices can be viewed as small, complex organizations (microsystems) that produce services for specific patient populations. These services can be greatly improved by embedding measurement into the flow of daily work in the practice. WHY DO IT?: Four good reasons to build measures into daily medical practice are to (1) diagnose strengths and weaknesses in practice performance; (2) improve and innovate in providing care and services using improvement research; (3) manage patients and the practice; and (4) evaluate changes in results over time. It is helpful to have a "physiological" model of a medical practice to analyze the practice, to manage it, and to improve it. One model views clinical practices as microsystems that are designed to generate desired health outcomes for specific subsets of patients and to use resources efficiently. This article provides case study examples to show what an office-based practice might look like if it were using front-line measurement to improve care and services most of the time and to conduct clinical improvement research some of the time. WHAT ARE THE PRINCIPLES FOR USING DATA TO IMPROVE PROCESSES AND OUTCOMES OF CARE?: Principles reflected in the case study examples--such as "Keep Measurement Simple. Think Big and Start Small" and "More Data Is Not Necessarily Better Data. Seek Usefulness, Not Perfection, in Your Measures"--may help guide the development of data to study and improve practice. HOW CAN A PRACTICE START TO USE DATA TO IMPROVE CARE AND CONDUCT IMPROVEMENT RESEARCH?: Practical challenges are involved in starting to use data for enhancing care and improvement research. To increase the odds for success, it would be wise to use a change management strategy to launch the startup plan. Other recommendations include "Establish a Sense of Urgency. (Survival Is Not Mandatory)" and "Create the Guiding Coalition. (A Small, Devoted Group of People Can Change the World)." SUMMARY: Over the long term, we must transform thousands of local practice cultures so that useful data are used every day in countless ways to assist clinicians, support staff, patients, families, and communities.

Looking at care from the inside out: a conceptual approach to geriatric care.

Today, managing care from the "outside in" is the predominant model for changing health care. The risk of this outside-in approach is that the health care system may lose sight of the people and communities for which it serves and cares. In this article, an "inside-out" model for viewing health care in a geriatric population is presented from the perspective of patients and providers, placing the provider in a proactive rather than reactive role. By focusing attention on the outcomes or value a patient is experiencing, providers are challenged to consider new ways of managing care.

Assessing access as a first step toward improving the quality of care for very old adults.

Understanding the barriers to obtaining care that the population of people age 80 and older (80+) experiences is one of the first steps toward developing organizational and clinical strategies aimed at improving care. This article reviews the data from the 80+ Project's survey to assess the prevalence of barriers to care and identify the characteristics that place the 80+ population at risk. Barriers to access for older adults occur on many levels. Ultimately, the ability to improve health outcomes through reducing barriers to care is dependent on the effectiveness and quality of care received. By recognizing the barriers to care that limit access, health care professionals can begin to develop strategies to eliminate these barriers and improve the health care of older adult patients.

The population of people age 80 and older: a sentinel group for understanding the future of health care in the United States.

Those age 80 years or older (80+) constitute a relatively small percentage of a health care system's population. However, because of the associated risks, costs, and variation of their medical care, 80+ patients are sentinel for elder care quality. This article describes the survey components of the 80+ Project. This project was designed to help clinicians gain a complete picture of the special needs of the 80+ population. The information about these patients' health and functional status, how they utilize health care resources, how they obtain access to care, and their satisfaction with the care is useful for clinicians who wish to improve health care delivery.

Geriatric education: a system approach.

This article describes an institutionwide geriatric educational initiative (called Geriatrics Awareness Month) that provided didactic and formal experiential learning designed for health professionals. From an educational perspective, to learn geriatrics requires systems thinking, and, to learn systems thinking, geriatrics provides an excellent clinical context. The authors evaluated the didactic and experiential aspects of Geriatric Awareness Month. For attendees of didactic sessions, the availability of pocket-sized educational materials was deemed most valuable. Despite busy schedules, house staff were able to make a change in their practice and study the effect of this change.

Implementing a strategy for improving care: lessons from studying those age 80 and older in a health system.

This article describes some potential solutions to the many practical barriers that arise when trying to improve clinical care in everyday practice. A useful mnemonic for incorporating measurement into daily work is called GAPS--setting Goals, Assessing the current processes, Planning a new approach, and Starting it. The 80+ Project represents a foundation of information and offers promise to create durable, productive interactions for elderly individuals and their health care providers.

Building measurement and data collection into medical practice.

Clinicians can use data to improve daily clinical practice. This paper offers eight principles for using data to support improvement in busy clinical settings: 1) seek usefulness, not perfection, in the measurement; 2) use a balanced set of process, outcome, and cost measures; 3) keep measurement simple (think big, but start small); 4) use qualitative and quantitative data; 5) write down the operational definitions of measures; 6) measure small, representative samples; 7) build measurement into daily work; and 8) develop a measurement team. The following approaches to using data for improvement are recommended. First, begin with curiosity about outcomes or a need to improve results. Second, try to avoid knee-jerk, obstructive criticism of proposed measurements. Instead, propose solutions that are practical, goal-oriented, and good enough to start with. Third, gather baseline data on a small sample and check the findings. Fourth, try to change and improve the delivery process while gathering data. Fifth, plot results over time and analyze them by using a control chart or other graphical method. Sixth, refine your understanding of variation in processes and outcomes by dividing patients into clinically homogeneous subgroups (stratification) and analyzing the results separately for each subgroup. Finally, make further changes while measuring key outcomes over time. Measurement and improvement are intertwined; it is impossible to make improvements without measurement. Measuring and learning from each patient and using the information gleaned to test improvements can become part of daily medical practice in local settings.

Continually improving the health and value of health care for a population of patients: the panel management process.

Today's primary care provider faces the challenge of caring for individual patients as well as caring for populations of patients. This article offers a model--the panel management process--for understanding and managing these activities and relationships. The model integrates some of the lessons learned during the past decade as we have worked to gain an understanding of the continual improvement of health care after we have understood that care as a process and system.

On the use of contour maps in the analysis of spread of communicable disease.

The co-ordinates of the dwellings where cases of variola minor (alastrim) occurred during a small epidemic were used in a worked example of contour mapping of disease spread. The contoured variable was the date of onset, relative to an arbitrary base date, of the case introducing the disease into each of twenty-two households. Three contour maps prepared with slightly different computer programmes or dates exhibited similar concentric loops whose centres were close to the first infected household. The average rate of spread of the disease was estimated by regression of the number of days to onset of the first case in the household on the average distance from an arbitrary origin to the relevant contour line. The calculated average rate of spread was 1.22 metres per day. An additional map was contoured using the cumulative number of cases as the contoured variable, relative to the onset of the example epidemic.