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Measuring Efficiency in Health Care
With the healthcare sector accounting for a sizeable proportion of national expenditures, the pursuit of efficiency has become a central objective of policymakers within most health systems. However, the analysis and measurement of efficiency is a complex undertaking, not least due to the multiple objectives of health care organizations and the many gaps in information systems. In response to this complexity, research in organizational efficiency analysis has flourished. This 2006 book examines some of the most important techniques currently available to measure the efficiency of systems and organizations, including data envelopment analysis and stochastic frontier analysis, and also presents some promising new methodological approaches. Such techniques offer the prospect of many new and fruitful insights into health care performance. Nevertheless, they also pose many practical and methodological challenges. This is an important critical assessment of the strengths and limitations of efficiency analysis applied to health and health care.
eBook
Data-driven healthcare
Data is revolutionizing the healthcare industry. With more data available than ever before, and applying the right analytics you can spur growth. Benefits extend to patients, providers, and board members, and the technology can make centralized patient management a reality. Despite the potential for growth, many in the industry and government are questioning the value of data in health care, wondering if it's worth the investment. This book tackles the issue and proves why BI is not only worth it, but necessary for industry advancement. Madsen challenges the notion that data has little value in healthcare, and shows how BI can ease regulatory reporting pressures and streamline the entire system as it evolves. She illustrates how a data-driven organization is created, and how it can transform the industry. --
eBook
Quality management in a lean health care environment
Quality in a lean health care setting has one ultimate goal-to improve care delivery and value for the patient. The purpose of this book is to provide a blueprint to hospitals, healthcare organizations, leaders, and patient-facing workers with tools, training, and ideas to address quality within their organization. Examples from health care an other industries are provider to illustrate lean methodology and learn their application in quality. The reader can learn how other organizations improve quality, what their roles are, and what they do daily. By the end of the book, you will have learned actionable concepts and have the tools and resources to start improving quality.
eBook
Millions of black people affected by racial bias in health-care algorithms
Study reveals rampant racism in decision-making software used by US hospitals -- and highlights ways to correct it.
Journal Article
Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges
This study examines the current state of artificial intelligence (AI)-based technology applications and their impact on the healthcare industry. In addition to a thorough review of the literature, this study analyzed several real-world examples of AI applications in healthcare. The results indicate that major hospitals are, at present, using AI-enabled systems to augment medical staff in patient diagnosis and treatment activities for a wide range of diseases. In addition, AI systems are making an impact on improving the efficiency of nursing and managerial activities of hospitals. While AI is being embraced positively by healthcare providers, its applications provide both the utopian perspective (new opportunities) and the dystopian view (challenges to overcome). We discuss the details of those opportunities and challenges to provide a balanced view of the value of AI applications in healthcare. It is clear that rapid advances of AI and related technologies will help care providers create new value for their patients and improve the efficiency of their operational processes. Nevertheless, effective applications of AI will require effective planning and strategies to transform the entire care service and operations to reap the benefits of what technologies offer.
Journal Article
The Agreement Between Virtual Patient and Unannounced Standardized Patient Assessments in Evaluating Primary Health Care Quality: Multicenter, Cross-sectional Pilot Study in 7 Provinces of China
The unannounced standardized patient (USP) is the gold standard for primary health care (PHC) quality assessment but has many restrictions associated with high human and resource costs. Virtual patient (VP) is a valid, low-cost software option for simulating clinical scenarios and is widely used in medical education. It is unclear whether VP can be used to assess the quality of PHC.
This study aimed to examine the agreement between VP and USP assessments of PHC quality and to identify factors influencing the VP-USP agreement.
Eleven matched VP and USP case designs were developed based on clinical guidelines and were implemented in a convenience sample of urban PHC facilities in the capital cities of the 7 study provinces. A total of 720 USP visits were conducted, during which on-duty PHC providers who met the inclusion criteria were randomly selected by the USPs. The same providers underwent a VP assessment using the same case condition at least a week later. The VP-USP agreement was measured by the concordance correlation coefficient (CCC) for continuity scores and the weighted κ for diagnoses. Multiple linear regression was used to identify factors influencing the VP-USP agreement.
Only 146 VP scores were matched with the corresponding USP scores. The CCC for medical history was 0.37 (95% CI 0.24-0.49); for physical examination, 0.27 (95% CI 0.12-0.42); for laboratory and imaging tests, -0.03 (95% CI -0.20 to 0.14); and for treatment, 0.22 (95% CI 0.07-0.37). The weighted κ for diagnosis was 0.32 (95% CI 0.13-0.52). The multiple linear regression model indicated that the VP tests were significantly influenced by the different case conditions and the city where the test took place.
There was low agreement between VPs and USPs in PHC quality assessment. This may reflect the \"know-do\" gap. VP test results were also influenced by different case conditions, interactive design, and usability. Modifications to VPs and the reasons for the low VP-USP agreement require further study.
Journal Article
Correction: Access to health services for men who have sex with men and transgender women in Beira, Mozambique: A qualitative study
[This corrects the article DOI: 10.1371/journal.pone.0228307.].
Journal Article
Strengthening multi-sectoral collaboration on critical health issues: One Health Systems Mapping and Analysis Resource Toolkit (OH-SMART) for operationalizing One Health
Addressing critical global health issues, such as antimicrobial resistance, infectious disease outbreaks, and natural disasters, requires strong coordination and management across sectors. The One Health approach is the integrative effort of multiple sectors working to attain optimal health for people, animals, and the environment, and is increasingly recognized by experts as a means to address complex challenges. However, practical application of the One Health approach has been challenging. The One Health Systems Mapping and Analysis Resource Toolkit (OH-SMART) introduced in this paper was designed using a multistage prototyping process to support systematic improvement in multi-sectoral coordination and collaboration to better address complex health concerns through an operational, stepwise, and practical One Health approach. To date, OH-SMART has been used to strengthen One Health systems in 17 countries and has been deployed to revise emergency response frameworks, improve antimicrobial resistance national action plans and create multi agency infectious disease collaboration protocols. OH-SMART has proven to be user friendly, robust, and capable of fostering multi-sectoral collaboration and complex system-wide problem solving.
Journal Article
How is AMSTAR applied by authors - a call for better reporting
The assessment of multiple systematic reviews (AMSTAR) tool is widely used for investigating the methodological quality of systematic reviews (SR). Originally, AMSTAR was developed for SRs of randomized controlled trials (RCTs). Its applicability to SRs of other study designs remains unclear. Our objectives were to: 1) analyze how AMSTAR is applied by authors and (2) analyze whether the authors pay attention to the original purpose of AMSTAR and for what it has been validated.
We searched MEDLINE (via PubMed) from inception through October 2016 to identify studies that applied AMSTAR. Full-text studies were sought for all retrieved hits and screened by one reviewer. A second reviewer verified the excluded studies (liberal acceleration). Data were extracted into structured tables by one reviewer and were checked by a second reviewer. Discrepancies at any stage were resolved by consensus or by consulting a third person. We analyzed the data descriptively as frequencies or medians and interquartile ranges (IQRs). Associations were quantified using the risk ratio (RR), with 95% confidence intervals.
We identified 247 studies. They included a median of 17 reviews (interquartile range (IQR): 8 to 47) per study. AMSTAR was modified in 23% (57/247) of studies. In most studies, an AMSTAR score was calculated (200/247; 81%). Methods for calculating an AMSTAR score varied, with summing up all yes answers (yes = 1) being the most frequent option (102/200; 51%). More than one third of the authors failed to report how the AMSTAR score was obtained (71/200; 36%). In a subgroup analysis, we compared overviews of reviews (n = 154) with the methodological publications (n = 93). The overviews of reviews were much less likely to mention both limitations with respect to study designs (if other studies other than RCTs were included in the reviews) (RR 0.27, 95% CI 0.09 to 0.75) and overall score (RR 0.08, 95% CI 0.02 to 0.35).
Authors, peer reviewers, and editors should pay more attention to the correct use and reporting of assessment tools in evidence synthesis. Authors of overviews of reviews should ensure to have a methodological expert in their review team.
Journal Article