Agenda

Bringing the benefits from AI efforts to the frontline workers continues to be a struggle across major healthcare organizations.  We worked on a novel, practical approach to directly take on the workflows of healthcare workers.  This session shares the successes and failures in our attempts, and the AI's introduced via this approach to achieve efficiency and/or outcome goals. This practical workflow approach uses AI as tools, hence can deploy various AI’s for a variety of problems including patient status tracking and task automation. AI's being directly in the workflow also enables continuous learning, process improvement, and optimization toward specific goals.

Until recently, healthcare has not understood root causes of diseases well enough for prevention; the main approach has historically been to treat patients after onset. While primary prediction scoring systems are routine for CVD patients, the goal is to reach patients before primary events occur. Amgen and a startup partner are co-developing a machine learning solution that uses existing EMR data to develop statistical and machine learning models predicting secondary CVD events. Having more accurate risk prediction models could significantly impact approaches to disease prevention. Session will also cover role of partnership in sourcing, prototyping, piloting, and scaling novel technologies.

A frequent criticism of the use of machine learning models as compared to human analysis is that ML models are "black boxes" and uninterpretable. Recent advancements in the field of explainable AI allow us to understand what factors influenced both individual predictions and aggregate model behaviors. We will revisit a case study from another PAW conference on predicting hospital readmissions, except this time we will use open-source software and dive into the 'why' with various visualizations that explain the model's behavior.

Emergency departments have seen a dramatic increase in the number of visits from elderly patients. Many elderly use a personal emergency response system (PERS) to signal for help in case of an incident such as a fall or breathing problems. At Partners Healthcare, we are testing a predictive model that uses PERS data to predict elderly at high risk of emergency department visits. Clinical staff from our homecare program perform interventions with high-risk patients. This presentation will cover the development of the predictive model and its deployment in a randomized controlled trial.

An emerging biotech company launched the first treatment option in an unestablished rare disease market. Extremely low prevalence, lack of physician awareness, no codified ICD-10 diagnosis code, and the lack of approved treatments resulted in significant mis-diagnosis, making the application of AI challenging. Addressing the challenge required combining first and third party de-identified data in a HIPAA-compliant workflow based on Swoop's prIvacy platform. Of the 84 start forms in the past 6 months, 24 (29%) were due to Swoop's AI model. Further validation is underway in a university hospital system, embedding predictions into clinical workflows to improve patient outcomes.

Healthcare has always used statistical analysis and analytic capabilities for accounting, reimbursement, actuarial and fiscal projection purposes. New developments in advanced statistical and predictive analytics techniques promise to revolutionize health and medical outcomes, and care delivery. These new techniques utilize modern machine learning and Artificial Intelligence methods to predict and prescribe at the individual level, instead of using traditional statistics. Learn how new machine learning techniques are being used for value-based purchasing, population health, healthcare consumerism and precision medicine. Peer into the future of Healthcare Data Science with predictions from industry leaders.

How much data is enough to build an accurate model?  This is often one of the first and most difficult questions to answer early in any machine learning project.  However, the quality and applicability of your data are more important considerations than quantity alone.  This talk presents some insights and lessons learned for gauging the suitability of electronic health record (EHR) training data for a desired project.  You will see how to determine if more data might increase accuracy and how to identify any weaknesses a model might have as a result of your current training data.

With the advent of big data and machine learning, there is an opportunity to combat rising healthcare costs by leveraging data in an ethical and privacy compliant way to establish more consistency and implantation of preventative care. We need to ensure there is a fundamental set of rules and responsibilities in place among healthcare organizations to protect their patient's privacy. In this presentation we will address this challenge and speak to the importance of creating an ethical and privacy compliant approach to aggregating multiple data sources which then can be used to improve patient outcomes.

Independent Pediatricians typically maintain daily patient volumes of 20-30 patients to keep their practices viable. Pediatricians also schedule appointments up to a year in advance, leading to as many as 15% of patients not showing up for appointments each day. The financial and clinical impact of these gaps in pediatric appointment books is substantial.

PCC and Rexer Analytics analyzed pediatric no-show patterns to identify the variables that truly affect appointment truancy. These insights were translated into interventions to reduce patient truancy. We present pediatric no-show patterns, key predictors, and the results several Pediatric practices are seeing with targeted interventions.