Perspectives and Best Practices for Artificial Intelligence and Continuously Learning Systems in Healthcare

Goals of this paper Healthcare is often a late adopter when it comes to new techniques and technologies; this works to our advantage in the development of this paper as we relied on lessons learned from CLS in other industries to help guide the content of this paper. Appendix V includes a number of example use cases of AI in Healthcare and other industries. This paper focuses on identifying unique attributes, constraints and potential best practices towards what might represent “good” development for Continuously Learning Systems (CLS) AI systems with applications ranging from pharmaceutical applications for new drug development and research to AI enabled smart medical devices. It should be noted that although the emphasis of this paper is on CLS, some of these issues are common to all AI products in healthcare. Additionally, there are certain topics that should be considered when developing CLS for healthcare, but they are outside of the scope of this paper. These topics will be briefly touched upon, but will not be explored in depth. Some examples include: Human Factors – this is a concern in the development of any product – what are the unique usability challenges that arise when collecting data and presenting the results? Previous efforts at generating automated alerts have often created problems (e.g. alert fatigue.) CyberSecurity and Privacy – holding a massive amount of patient data is an attractive target for hackers, what steps should be taken to protect data from misuse? How does the European Union’s General Data Protection Regulation (GDPR) impact the use of patient data? Legal liability – if a CLS system recommends action that is then reviewed and approved by a doctor, where does the liability lie if the patient is negatively affected? Regulatory considerations – medical devices are subject to regulatory oversight around the world; in fact, if a product is considered a medical device depends on what country you are in. AI provides an interesting challenge to traditional regulatory models. Additionally, some organizations like the FTC regulate non-medical devices. This paper is not intended to be a standard, nor is this paper trying to advocate for one and only one method of developing, verifying, and validating CLS systems – this paper highlights best practices from other industries and suggests adaptation of those processes for healthcare. This paper is also not intended to evaluate existing or developing regulatory, legal, ethical, or social consequences of CLS systems. This is a rapidly evolving subject with many companies, and now some countries, establishing their own AI Principles or Code of Conduct which emphasize legal and ethical considerations including goals and principles of fairness, reliability and safety, transparency around how the results of these learning systems are explained to the people using those systems5 . The intended audience of this paper are Developers, Researchers, Quality Assurance and Validation personnel, Business Managers and Regulators across both Medical Device and Pharmaceutical industries that would like to learn more about CLS best practices, and CLS practitioners wanting to learn more about medical device software development

Innovative Professional Network Echo Method Improves Recruitment of Diverse and Multicultural Students to Health Administration

Health administration professions do not reflect US demographic and economic structure. Pragmatically, new programs are resource-limited. Novel, reliable and valid recruitment and admission strategies are needed to address this gap. We aimed to create replicable, low-cost recruitment to support multicultural diversity at the graduate level and subsequently, in healthcare leadership. A pilot survey of healthcare leaders and students identified top trends, hiring needs and sustainable opportunities. Health data analytics, outcomes research and process improvement were consistently identified by both groups. The new MS in Health Economic and Clinical Outcomes Research program emphasized these areas, ensuring upward mobility of graduates. Following standard process improvement methodologies, recruitment processes were mapped and gaps identified. The innovative Professional Network Echo Method (PN ECHO) increased the percent of multicultural and racially diverse students by 32% and 46%, respectively, using a targeted systems approach flowchart of LinkedIn™, Slate™ enrollment management software, with strategies to connect, funnel and evaluate diverse potential students. To support students of vastly variable backgrounds, professional skills were emphasized throughout the program, with 100% retention. Consistent processes and forms support measurable inclusivity and a sustainable open network, with minimal training. PN ECHO improves potential for increased diversity and multicultural leadership in the executive suite

Newly Developed and Validated Eosinophilic Esophagitis Histology Scoring System and Evidence that it Outperforms Peak Eosinophil Count for Disease Diagnosis and Monitoring

Eosinophilic esophagitis is diagnosed by symptoms, and at least 15 intraepithelial eosinophils per high power field in an esophageal biopsy. Other pathologic features have not been emphasized. We developed a histology scoring system for esophageal biopsies that evaluates eight features: eosinophil density, basal zone hyperplasia, eosinophil abscesses, eosinophil surface layering, dilated intercellular spaces, surface epithelial alteration, dyskeratotic epithelial cells and lamina propria fibrosis. Severity (grade) and extent (stage) of abnormalities were scored using a 4 point scale (0 normal; 3 maximum change). Reliability was demonstrated by strong to moderate agreement among 3 pathologists who scored biopsies independently (p≤0.008). Several features were often abnormal in 201 biopsies (101 distal, 100 proximal) from 104 subjects (34 untreated, 167 treated). Median grade and stage scores were significantly higher in untreated compared to treated subjects (p≤0.0062). Grade scores for features independent of eosinophil counts were significantly higher in biopsies from untreated compared to treated subjects (basal zone hyperplasia p≤0.024 and dilated intercellular spaces p≤0.005), and were strongly correlated (r-square\u3e0.67). Principal components analysis identified 3 principal components that explained 78.2% of the variation in the features. In logistic regression models, 2 principal components more closely associated with treatment status than log distal peak eosinophil count (r-square 17, area under the curve 77.8 vs r-square 9, area under the curve 69.8). In summary, the eosinophilic esophagitis histology scoring system provides a method to objectively assess histologic changes in the esophagus beyond eosinophil number. Importantly, it discriminates treated from untreated patients, uses features commonly found in such biopsies, and is utilizable by pathologists after minimal training. These data provide rationales and a method to evaluate esophageal biopsies for features in addition to peak eosinophil count