IMPROVING DIAGNOSIS IN HEALTH CARE
Institute of Medicine
September 2015
Quality Chasm Series
Improving Diagnosis in Health Care is the most recent report, released September 22, 2015, from the Institute of Medicine (IOM). This report is a continuation of the landmark IOM reports To Err Is Human: Building A Safer Health System (2000) and Crossing the Quality Chasm: A New Health System for the 21st Century (2001).
Although we have made tremendous strides since the first 2 reports, we are alerted to a new threat-diagnostic error. The first 2 reports from IOM alerted the public and united all health care professionals to eliminate errors and create a safer health care system. The current report illuminates a new threat and again calls us to action as it states "Improving the diagnostic process is not only possible, but also represents a moral, professional, and public health imperative.1,2 This report concluded that most people experience at least 1 diagnostic error in their lifetime and serves as an urgent warning for health care providers and patients to address this challenge. This new report frames error from the patient perspective and defines diagnosis error as "the failure to (a) establish an accurate and timely explanation of the patient's health problem(s) or (b) communicate that explanation to the patient."1,2
Diagnostic errors may cause harm if care is delayed, omitted, or delivered in error. This type of error may inflict direct physical harm or have significant psychological or financial repercussions. The report states that this goes beyond the hospital experience and estimates that 5% of US adults who seek outpatient care each year experience a diagnostic error.1,2
Additional information states that "Postmortem examination research spanning decades has shown that diagnostic errors contribute to approximately 10% of patients deaths, and medical record reviews suggest that they may account for 6% to 17% of adverse events in hospitals."1,2
In response, the committee has outlined 8 goals to reduce diagnostic error and presents recommendations for improvements in diagnosis. The goals are anchored in collaboration, education, technologies, culture, systems redesign, and continued research
1. Facilitate more effective teamwork in the diagnostic process among health care professionals, patients, and their families.
2. Enhance health care professional education and training in the diagnostic process.
3. Ensure that health information technology supports patients and health care professionals in the diagnostic process.
4. Develop and deploy approaches to identify, learn from, and reduce diagnostic errors and near misses in clinical practice.
5. Establish a work system and culture that support the diagnostic process and improvements in diagnostic performance.
6. Develop a reporting system and medical liability system that facilitates improved diagnosis through learning from diagnostic error and near misses.
7. Design a payment and care delivery environment that supports the diagnostic process.
8. Provide dedicated funding for research on the diagnostic process and diagnostic error.1,2
The report supports a dedicated focus with collaborative activities that includes providers, care organizations, patients, and families, as well as researchers and policy makers.
Note: IOM reports provide objective and straightforward advice to decision makers and the public. This site includes IOM reports published after 1998. A complete list of IOM's publications, from its establishment in 1970 through June 30, 2014, is available as a PDF. See more at http://iom.nationalacademies.org/Reports.aspx#sthash.108Zcfpc.dpuf.
HEALTH AFFAIRS BLOG
Lean: A Comprehensive Approach to the Transformation Our Health Care System Needs
Patricia Gabow and Patrick H. Conway
August 13, 2015
http://healthaffairs.org/blog/2015/08/13/lean-a-comprehensive-approach-to-the-tr
Dr Patricia Gabow and Patrick Conway have experience in developing lean systems and presenting their work in various forums. Gabow is coauthor of the recent (2015) book, The Lean Prescription: Powerful Medicine for Our Ailing Healthcare System.
Their work continues to inspire and teach. This is a wonderful blog to follow if you believe that systems transformation requires a disciplined and structured approach-something that "LEAN" can provide.
Toyota Production System or "Lean" is an effective approach to improve health care. Gabow and Conway explain how "the power of Lean lies in both its philosophy and its robust tool set, which takes aim at eliminating waste from the customer (in our case the patient) perspective."
This piece expands on some of the key components of lean thinking such as the following:
1. First, build people
2. Eliminate waste
3. Learn about the benefits of Lean Adoption
4. Then spread the Lean model
Although there are many approaches to improving care, the authors remind us "Lean is a respectful, disciplined, and powerful approach to achieve the broad, comprehensive, and important change that our health system needs."
Hastie, Tibshirani, and Friedman. The Elements of Statistical Learning (2nd edition): Data Mining, Inference, and Prediction. New York: Springer-Verlag; 2009. 763 pages
The field of statistics is changing. The digital revolution, and our ability to collect more data than imaginable, require that we are skilled in statistical analyses in creative and complex ways. Our ability to collect, store, and process data has grown amid these revolutions, demanding the field of statistics to create its own revolution to keep up with big data and encourage new ways of looking at all data. Although traditional, classic methods remain at the core of statistical inference, they may not be sufficient for many current data analysis.
Some modern analyses are focused on finding unknown structures with large data sets. These applications have many names, originally called data mining, but they are now more generally referred to as modern statistical learning methods.
Given the ubiquity of these types of problems, and the success data analysts have had using these methods, it is important for the modern statistician to keep current with these methodologies.
This second edition features many topics not covered in the original version, including graphical models, random forests, ensemble methods, least angle regression, and path algorithms for the lasso, nonnegative matrix factorization, and spectral clustering. There is also a chapter on methods for "wide" data (p bigger than n), including multiple testing and false discovery rates.
This text serves to provide a resource for modern researchers and data users to help each other keep current with new methodologies. This book is available as an e-book and may be downloaded from selected sites such as http://statweb.stanford.edu/~tibs/ElemStatLearn/.
To download the full report and to find additional resources, visit http://iom.nationalacademies.org/reports/2015/improving-diagnosis-in-healthcare.
References