Extending Mayo Clinic’s Reach Internationally

To support Mayo Clinic’s strategy to extend its reach internationally, ME&C was engaged to provide consulting services for a medical facility in Abu Dhabi, United Arab Emirates (UAE). The goal of this engagement was to develop a successful and agile activation plan and assist in the seamless transition of patients, staff and services from the existing Mafraq facility to the new Sheik Shakhbout Medical Center (SSMC) within a seven-month time frame.

A team of Health Systems Engineers and a Project Manager from ME&C was engaged to design and execute an activation plan for the new facility. These staff members played a key role in quickly understanding the current and desired future state, conducting a gap analysis, leveraging best practices identified through literature reviews and previous engagements, and then applying an engineering approach (including analytics, forecasting and modeling) and project management methodology to create a robust activation plan specific for SSMC. Upon completion of SSMC activation, no significant patient safety issues, regulatory findings, security events or adverse media reports were identified.

Machine Learning transforms Clinical Trial Matching at Mayo Clinic

Mayo Clinic partnered with IBM to leverage the Watson Clinical Trials Matching (CTM) solution to assist patient care and research teams with the complex process of identifying potential patient-to-trial matches. This solution electronically compares unstructured text in the electronic health record data against eligibility criteria for all cancer clinical trials open at Mayo Clinic. The IBM Watson CTM solution is a web-based cognitive computing system which utilizes natural language processing. 

ME&C assisted with the integration of the IBM Watson CTM cognitive computing system with a skilled patient screening team and clinical providers to facilitate the complex process of identifying potential patient-to-trial matches. This transformational strategy enabled high volume patient screening for trials with significant benefits to Mayo patients and staff.

Mayo Clinic launches its first Platform Initiative

ME&C engaged with leaders and staff from Mayo Clinic Business Development and nference, a Cambridge-based augmented intelligence company, to enable the development of the Clinical Data Analytics Platform (CDAP). This platform is designed to organize and make available Mayo Clinic’s clinical data for the purpose of creating insights and discoveries aimed to advance medical knowledge and lead to the development of new therapies for patients. 

ME&C staff partnered with colleagues from Mayo Clinic Business Development, Clinical Practice and Research to develop a data inventory, data transfer processes, policies and procedures to ensure safeguards against patient identity and later, pair de-identified data with genomics data to promote knowledge delivery.

Using Artificial Intelligence to Improve Patient Volume Management

ME&C staff used artificial intelligence (AI) to identify predictive indicators and actionable recommendations to provide prioritized outpatient visits for patients most likely to benefit from downstream services such as diagnostic testing and procedures. For this initiative, the outcome from the machine learning heuristics prototype included variables or a combination of variables to help redefine the decision tree and/or change appointments slots to drive downstream services. Previously unidentified factors influencing downstream services are now revealed via machine learning heuristics and can be incorporated into the decision making process.

Outpatient access decision trees are now dynamic and allow for better patient volume management. This focused approach enables capacity for more appropriate appointment slots and, in some cases, can reduce outpatient volumes to help lower physician stress often associated with high patient volumes. Lastly, a well-performing predictive model can provide decision makers with the necessary metrics to better manage seasonal change in patient volumes.

Artificial Intelligence (AI) Aimed to Reduce Hospital Readmissions

ME&C staff incorporated the use of a vended AI tool to create a solution to reduce hospital readmissions. In this scenario, a machine learning algorithm is used to identify inpatients at risk of readmission based on electronic health record information and social determinants of health. The AI tool then identifies appropriate patient-specific interventions to prevent readmission and presents these recommendations to the clinical team in real time during hospitalization to effectively plan for discharge. The impact of this pilot was two-fold:  to reduce readmissions and equally important, to increase staff confidence in the use of the AI tool to determine the level of effectiveness in reducing readmission rates.