Increased Values In Patients Research Articles

Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence: A Critical Analysis Review.

» The application of artificial intelligence (AI) in the field of orthopaedic surgery holds potential for revolutionizing health care delivery across 3 crucial domains: (I) personalized prediction of clinical outcomes and adverse events, which may optimize patient selection, surgical planning, and enhance patient safety and outcomes; (II) diagnostic automated and semiautomated imaging analyses, which may reduce time burden and facilitate precise and timely diagnoses; and (III) forecasting of resource utilization, which may reduce health care costs and increase value for patients and institutions.» Computer vision is one of the most highly studied areas of AI within orthopaedics, with applications pertaining to fracture classification, identification of the manufacturer and model of prosthetic implants, and surveillance of prosthesis loosening and failure.» Prognostic applications of AI within orthopaedics include identifying patients who will likely benefit from a specified treatment, predicting prosthetic implant size, postoperative length of stay, discharge disposition, and surgical complications. Not only may these applications be beneficial to patients but also to institutions and payors because they may inform potential cost expenditure, improve overall hospital efficiency, and help anticipate resource utilization.» AI infrastructure development requires institutional financial commitment and a team of clinicians and data scientists with expertise in AI that can complement skill sets and knowledge. Once a team is established and a goal is determined, teams (1) obtain, curate, and label data; (2) establish a reference standard; (3) develop an AI model; (4) evaluate the performance of the AI model; (5) externally validate the model, and (6) reinforce, improve, and evaluate the model's performance until clinical implementation is possible.» Understanding the implications of AI in orthopaedics may eventually lead to wide-ranging improvements in patient care. However, AI, while holding tremendous promise, is not without methodological and ethical limitations that are essential to address. First, it is important to ensure external validity of programs before their use in a clinical setting. Investigators should maintain high quality data records and registry surveillance, exercise caution when evaluating others' reported AI applications, and increase transparency of the methodological conduct of current models to improve external validity and avoid propagating bias. By addressing these challenges and responsibly embracing the potential of AI, the medical field may eventually be able to harness its power to improve patient care and outcomes.

Implementing a Radiology Residency Quality Curriculum to Develop Physician Leaders and Increase Value for Patients.

Diagnostic and interventional radiology residency programs must educate trainees on quality and patient safety topics to meet board requirements and prepare residents to become effective physician leaders. A quality curriculum should encompass process improvement methodology as well as instruction about crucial patient safety subjects. The authors have developed a standardized and structured approach to fulfill this need using didactic and experiential learning. The educational format includes short lectures, peer-to-peer instruction, and self-study, with the value of presented information reinforced by physician leaders and process improvement specialists. Equally important is a structured experience in departmental quality improvement wherein trainees learn the collaborative nature of effective durable process change in areas of interest to them. This curriculum is implemented during the 3rd year of radiology residency to leverage residents' knowledge and experience with radiology workflows and proximity to the American Board of Radiology Core Exam. Feedback from educators and trainees as well as objective examination data support this approach. This article shares guidance and lessons learned from the authors' radiology residency educational efforts and offers a framework for successful implementation of a comprehensive quality curriculum at any residency training program. This curriculum serves the dual purpose of developing skilled future physician leaders and promoting value for patients. ©RSNA, 2020.

Good, better, best? A comprehensive comparison of healthcare providers’ performance: An application to physiotherapy practices in primary care

Most payment methods in healthcare stimulate volume-driven care, rather than value-driven care. Value-based payment methods such as Pay-For-Performance have the potential to reduce costs and improve quality of care. Ideally, outcome indicators are used in the assessment of providers’ performance. The aim of this paper is to describe the feasibility of assessing and comparing the performances of providers using a comprehensive set of quality and cost data. We had access to unique and extensive datasets containing individual data on PROMs, PREMs and costs of physiotherapy practices in Dutch primary care. We merged these datasets at the patient-level and compared the performances of these practices using case-mix corrected linear regression models. Several significant differences in performance were detected between practices. These results can be used by both physiotherapists, to improve treatment given, and insurers to support their purchasing decisions. The study demonstrates that it is feasible to compare the performance of providers using PROMs and PREMs. However, it would take an extra effort to increase usefulness and it remains unclear under which conditions this effort is cost-effective. Healthcare providers need to be aware of the added value of registering outcomes to improve their quality. Insurers need to facilitate this by designing value-based contracts with the right incentives. Only then can payment methods contribute to value-based healthcare and increase value for patients.

The Case for Medicaid Expansion in North Carolina.

North Carolina's refusal to expand its Medicaid program has left many thousands of North Carolinians without health insurance and has imposed unnecessary costs on all of the state's residents through higher premiums in the state's health insurance marketplace. Expanding Medicaid is the most efficient way to extend coverage to the state's uninsured population, and expansion would bring a substantial amount of new federal money into the state. This money can serve as a catalyst for ambitious reforms to the Medicaid program that can lower costs, improve quality of care, and increase value for patients and taxpayers alike.

Improving the value of healthcare delivery using publicly available performance data in Wisconsin and California

The healthcare industry must change in order to provide higher quality care and lower costs for patients; one method to improve both cost and quality used in Wisconsin and California is leveraging publicly reported claims and costs data. Wisconsin has been building comprehensive, publicly available clinical and administrative data sets: the Wisconsin Collaborative for Healthcare Quality (WCHQ) established in 2003 and the Wisconsin Health Information Organization (WHIO) established in 2009. The WCHQ and the WHIO allow physician groups to compare themselves with one another on cost and quality across 920 distinct episode treatment groups (ETGs). The ETGs include all components of care for a specific disease during a defined period. Since 2002 California has developed public reporting of quality data for physician groups and health plans through its Integrated Healthcare Association (IHA) and since 2008 its Right Care Initiative (RCI). In both states these data are used to identify best practices and opportunities for improvement, enhance care outcomes, and increase value for patients.