Physician Burnout on the Rise
Burnout and satisfaction with work-life balance in US physicians worsened from 2011 to 2014, and more than half of US physicians are now experiencing professional burnout, according to results of a study published in Mayo Clinic Proceedings.
From August 28, 2014 to October 6, 2014, Tait D. Shanafelt, MD and colleagues surveyed US physicians and a probability-based sample of the general US population. Burnout was measured using validated metrics, and satisfaction with work-life balance was assessed using standard tools.
Of the 35,922 physicians who received an invitation to participate, 6,880—19.2 percent—completed surveys. When assessed using the Maslach Burnout Inventory, 54.4 percent of the physicians reported at least one symptom of burnout in 2014 compared with 45.5 percent in 2011. Satisfaction with work-life balance also declined in physicians between 2011 and 2014 (48.5 percent vs 40.9 percent). Substantial differences in rates of burnout and satisfaction with work-life balance were observed by specialty. In contrast, researchers reported that minimal changes in burnout or satisfaction with work-life balance were observed between 2011 and 2014 in the probability-based samples of working US adults, resulting in an increasing disparity in burnout and satisfaction with work-life balance in physicians relative to the general US working population. After pooled multivariate analysis adjusting for age, sex, relationship status, and hours worked per week, physicians remained at an increased risk of burnout and were less likely to be satisfied with work-life balance
The researchers say evidence indicates that burnout leads to poor care, physician turnover, and a decline in the overall quality of the health care system. They add that the problem of physician burnout is largely a system issue and that more needs to be done by healthcare organizations to help physicians by improving the efficiency of the practice environment, reducing clerical burden, and providing physicians greater flexibility and control over work.
AMA Launches Health2047
Building on its commitment to advance the practice of medicine and improve public health, the American Medical Association (AMA) invested $15 million to become founding partner of a healthcare company, Health2047, Inc., that will conduct rapid exploration of innovative solutions to the biggest challenges facing the nation's 1.1 million physicians and their patients.
A stand-alone, for-profit entity, Health2047 is an integrated innovation company that combines strategy, design, and venture disciplines, working in partnership with leading companies, physicians, and entrepreneurs to improve healthcare. Its new Silicon Valley-based innovation studio will draw upon the AMA's deep subject matter expertise and the organization's unique relationship with physicians nationwide to develop new products, tools, and resources that improve the practice of medicine and the delivery of health care to patients. Health2047 will collaborate with AMA content experts across a wide range of medical, health policy, and pragmatic practice areas. It will also integrate healthcare companies, technology companies, and entrepreneurs to co-develop, create, and spin out offerings that can have large scale, systemic impact on health care and medical practice.
Through its ongoing work, the AMA says it is providing opportunities for physicians to engage in innovation and share their ideas, expertise, and real-world perspective on the effectiveness of technology in medical practice settings. From revitalizing medical practices to ensuring that digital health helps provide high-quality patient care, the AMA is striving to help physicians navigate and succeed in a continually evolving healthcare environment.
Machine Learning, Big Data May Speed Advances in Plastic Surgery
Machine learning and big data may help advance plastic surgery, according to an article in the May issue of Plastic and Reconstructive Surgery.
In a nutshell, machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. “Machine learning has the potential to become a powerful tool in plastic surgery, allowing surgeons to harness complex clinical data to help guide key clinical decision-making,” writes Jonathan Kanevsky, MD a plastic surgeon at McGill University in Montreal, and colleagues.
In particular, machine learning analyzes historical data to develop algorithms. “Machine learning has already been applied, with great success, to process large amounts of complex data in medicine and surgery,” Dr. Kanevsky and coauthors write. Projects with healthcare applications include the IBM Watson Health cognitive computing system and the American College of Surgeons' National Surgical Quality Improvement Program. Plastic surgery can benefit from similar “objective and data-driven machine learning approaches”—particularly with the availability of the ASPS's ‘Tracking Operations and Outcomes for Plastic Surgeons' (TOPS) database.
Five key areas where machine learning shows promise for improving efficiency and clinical outcomes in plastic and/or reconstructive surgery include:
- Burn Surgery. A machine learning approach has already been developed to predict the healing time of burns, providing an effective tool for assessing burn depth. Algorithms could also be developed to enable rapid prediction of percentage of body surface area burned, they write.
- Microsurgery. A postoperative microsurgery application has been developed to monitor blood perfusion of tissue flaps, based on smartphone photographs. In the future, algorithms may be developed to aid in suggesting the best reconstructive surgery approach for individual patients.
- Craniofacial Surgery. Machine learning approaches for automated diagnosis of infant craniosynostosis have been developed. Future algorithms may be useful for identifying known and unknown genes responsible for cleft lip and palate.
- Hand and Peripheral Nerve Surgery. Machine learning approaches may be useful in predicting the success of tissue-engineered nerve grafts, developing automated controllers for hand and arm neuroprostheses in patients with high spinal cord injuries, and improving planning and outcome prediction in hand surgery.
- Aesthetic Surgery. Machine learning also has potential applications in cosmetic surgery—for example, predicting and simulating the outcomes of aesthetic facial surgery and reconstructive breast surgery.
Watch This Now
IMPROVE YOUR PRACTICE THROUGH PATIENT SURVEYS
Todd E. Schlesinger, MD spoke with Modern Aesthetics Journal Club host Rebecca Kazin, MD to discuss the varied applications of patient surveys in an aesthetics practice. They explore specific strategies for asking the right questions and how to interpret feedback to optimize practice efficiency and patient communications.
Visit ModernAesthetics.tv Search Key: Patient Surveys
The authors also foresee useful applications of machine learning to improve plastic surgery training.
AMA Continues Efforts to Improve Electronic Health Records
The American Medical Association (AMA) continues to pledge to work with the Department of Health & Human Services to improve the flow of electronic health information to patients and physicians to increase data sharing that will achieve healthier people and smarter spending. The AMA acknowledges that the lack of seamless data exchange continues to drag down physician efficiency and patient satisfaction when using these tools and that improving electronic health records (EHRs) will require a concerted effort of public and private stakeholders. The AMA strongly supports the building blocks of EHR interoperability: 1) Improved Consumer Access 2) No Information Blocking 3) The Use of Nationally Recognized Interoperability Standards.
The majority of hospitals and physicians already use certified electronic health records, but for many, the true utility of these products is still elusive.