Mayo Clinic: Multidisciplinary Teamwork, Physician-Led Governance, and Patient-Centered Culture Drive World-Class Health Care

Describes Fund-defined attributes of an ideal care delivery system, Mayo's model of multidisciplinary practice with salary-based compensation, and best practices, including a shared electronic health record and innovations to implement research quickly

Geisinger Health System: Achieving the Potential of System Integration Through Innovation, Leadership, Measurement, and Incentives

Presents a case study of a physician-led nonprofit healthcare group exhibiting the attributes of an ideal healthcare delivery system as defined by the Fund. Describes how its ProvenCare model improved clinical outcomes with reduced resource utilization

The New York City Health and Hospitals Corporation: Transforming a Public Safety Net Delivery System to Achieve Higher Performance

Describes the results of the public benefit corporation's improvement initiatives -- a common clinical information system for continuity, coordination on chronic disease management, teamwork and continuous innovation, and access to appropriate care

Organizing the U.S. Health Care Delivery System for High Performance

Analyzes the fragmentation of the healthcare delivery system and makes policy recommendations -- including payment reform, regulatory changes, and infrastructure -- for creating mechanisms to coordinate care across providers and settings

How to optimize the business model for ConsultaClick an innovative startup in the e-health Portuguese sector

Field lab: Entrepreneurial and innovative venturesThis work project is focused on the optimization of ConsultaClick Business Model. ConsultaClick is an innovative startup that plays in the e-health Portuguese sector, and is dedicated to the online medical consultation booking business. It offers for the first time choice, convenience and control of quality of healthcare service for patients as well as marketing and operational management tools for physicians. The main research question of this work project is “Why isn’t ConsultaClick generating the expected booking and new patients’ registration results?” In order to understand the low conversion rates achieved, business opportunities were analyzed considering both external and internal factors while identifying nexus causality explanation for such values. The answer to the research question was sought trough an issue tree method for the characterization of the problem, and the validation of different hypothesis through online surveys, semi-structured interviews and other complementary desk research, focusing the market, international benchmark and competitive assessment. Based on the results of such studies a set of strategies and recommendations were provided to improve the business model, fulfill customers’ needs and reach ConsultaClick’s growth

What do measures of patient satisfaction with the doctor tell us?

Objective: To gain an understanding of how patient satisfaction (PS) with the doctor (PSD) is conceptualized through an empirical review of how it is currently being measured. The content of PS questionnaire items was examined to (a) determine the primary domains underlying PSD, and (b) summarize the specific doctor-related characteristics and behaviors, and patient-related perceptions, composing each domain. Methods: A scoping review of empirical articles that assessed PSD published from 2000 to November 2013. MEDLINE and PsycINFO databases were searched. Results: The literature search yielded 1726 articles, 316 of which fulfilled study inclusion criteria. PSD was realized in one of four health contexts, with questions being embedded in a larger questionnaire that assessed PS with either: (1) overall healthcare, (2) a specific medical encounter, or (3) the healthcare team. In the fourth context, PSD was the questionnaire's sole focus. Five broad domains underlying PSD were revealed: (1) Communication Attributes; (2) Relational Conduct; (3) Technical Skill/Knowledge; (4) Personal Qualities; and (5) Availability/Accessibility. Conclusions: Careful consideration of measurement goals and purposes is necessary when selecting a PSD measure. Practice implications: The five emergent domains underlying PSD point to potential key areas of physician training and foci for quality assessment

Analysis of massive online medical consultation service data to understand physicians’ economic return: observational data mining study

Background: Online health care consultation has become increasingly popular and is considered a potential solution to health care resource shortages and inefficient resource distribution. However, many online medical consultation platforms are struggling to attract and retain patients who are willing to pay, and health care providers on the platform have the additional challenge of standing out in a crowd of physicians who can provide comparable services. Objective: This study used machine learning (ML) approaches to mine massive service data to (1) identify the important features that are associated with patient payment, as opposed to free trial–only appointments; (2) explore the relative importance of these features; and (3) understand how these features interact, linearly or nonlinearly, in relation to payment. Methods: The dataset is from the largest China-based online medical consultation platform, which covers 1,582,564 consultation records between patient-physician pairs from 2009 to 2018. ML techniques (ie, hyperparameter tuning, model training, and validation) were applied with four classifiers—logistic regression, decision tree (DT), random forest, and gradient boost—to identify the most important features and their relative importance for predicting paid vs free-only appointments. Results: After applying the ML feature selection procedures, we identified 11 key features on the platform, which are potentially useful to predict payment. For the binary ML classification task (paid vs free services), the 11 features as a whole system achieved very good prediction performance across all four classifiers. DT analysis further identified five distinct subgroups of patients delineated by five top-ranked features: previous offline connection, total dialog, physician response rate, patient privacy concern, and social return. These subgroups interact with the physician differently, resulting in different payment outcomes. Conclusions: The results show that, compared with features related to physician reputation, service-related features, such as service delivery quality (eg, consultation dialog intensity and physician response rate), patient source (eg, online vs offline returning patients), and patient involvement (eg, provide social returns and reveal previous treatment), appear to contribute more to the patient’s payment decision. Promoting multiple timely responses in patient-provider interactions is essential to encourage payment

HealthPartners: Consumer-Focused Mission and Collaborative Approach Support Ambitious Performance Improvement Agenda

Presents a case study of a nonprofit healthcare organization that exhibits the six attributes of an ideal healthcare delivery system as defined by the Fund, including information continuity, care coordination and transitions, and system accountability

Exploring achievement gamification on online medical quality based on machine learning and empirical analysis

How to improve online medical quality is an important challenge for practitioners of digital health platforms. Gamification creates new opportunities to deal with the problem persistent in online health services. To better understand the role of gamification in online health services context, this study intends to use the research method of machine learning and natural experiment to explore the impact of achievement gamification on online medical quality in online health services, as well as the moderating effects of doctors’ personality and image. Theoretically, this study will expand the application of game strategy in the field of healthcare, and make up for the deficiency of the effects of gamification on online medical quality. Practically, it provides guidance for promoting doctors\u27 online participation behavior, improves the quality of online health services, and suggests ways for optimizing the rational allocation of online health resources