Summer 2017
Greater Charlotte Healthcare Executives Group (GCHEG) Quarterly Newsletter Summer 2017
In This Issue
President's Message
Greetings from your Chapter President
Regent's Message
Message from Your ACHE Regent
Membership and Advancement
Congratulations New Fellows!
Welcome New GCHEG Members
Member Submitted Articles
Artificial Intelligence: Healthcare Can Benefit From Smart Use of Data
2017 GCHEG Annual Dinner
Career Articles
Key Components of a Career Plan
Upcoming Events
GCHEG Summer Networking Event
ACHE - National News
National News Q2 2017
Articles of Interest
6 Tips for Working With a Poor Team Player
Tapping Community Physicians for Innovation Ideas
Staying Connected
Engaging with GCHEG on Social Media
Email deliverability
Ensure delivery of Chapter E-newsletter (Disclaimer)
Newsletter Tools
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Member Submitted Articles
Artificial Intelligence: Healthcare Can Benefit From Smart Use of Data
By: Alan Cudney, RN-BC, CPHQ, PMP, FACHE

The term Artificial Intelligence (AI) is routinely used in the media. Through fictional movies like The Terminator series and I, Robot, we have become de-sensitized to the idea, and many view it as something that is still “way out there,” leaving consideration to aficionados of fiction. However, with advances in information technology and, more specifically, analytics, healthcare delivery will see big changes over the coming decades. Here are some possible uses for AI and examples of how it could work.

Intelligent clinical decision support at the point of care

 

An endocrinologist evaluates options for Mary, a patient with Type 2 diabetes, to help better manage her health. Using data sources aggregated and normalized across episodes and sites of care, the physician can instantly see her complete medical history, including a related hospitalization in a different city, her prescription refill history, and her completion of diabetes management education. As he begins to place an order for a new type of insulin, the clinical system responds that, due to Mary's clinical history and genetic map, a different drug combination would be more effective.

Predictive models that continuously re-evaluate health status and best ways to help people make healthy choices

 

Ron hates to fill out surveys and has missed two of his last five doctor appointments. He ignores voice mail but responds to text messages and automated medication reminders from his cell phone. AI is used to continuously update a predictive model, which projects the most effective way to communicate with Ron and encourage him to make healthy choices, based on his previous activities. The model includes data feeds from social media, healthcare patient portals, credit card companies and pharmacy benefit administrators.

 

In a like manner, an ACO or medical home will stratify groups of patients according to clinical measures, health status and lifestyle characteristics, automatically assigning patients to specific categories of care management intervention. AI revises the predictive models as new data are available. These models then re-run the cohort selection, risk stratification, and subsequent recommendations.

Medical supply companies will predict future supply and demand needs for durable medical equipment at the unit, facility or service area levels

 

Imagine AI software is able to predict that, based on public health and hospital data, demand for home oxygen will increase in a six-county area of the state. The software suggests re-direction of specific oxygen canisters volumes and numbers of delivery staff to that geographic area. The AI-driven models anticipate patient needs and respond much more quickly than traditional reporting and surveillance processes.

Pharmaceutical companies will customize prescription drugs, based upon understanding of individual DNA and likelihood of intended response

 

Marla is taking a new medication to control her hypertension. The molecular structure of the medication, as well as the dosage, have been slightly modified to account for her genetic predispositions and previous response to other anti-hypertensive medications. The predictive models continuously optimize themselves as new clinical data comes in from hospitals and doctor offices.

 

AI with deep learning of radiological images and nanotech sensors will take clinical decision support to a new level

 

Pathologic lesions can be missed on diagnostic images. Detecting these is often dependent upon the training and expertise of the individual clinician. AI with image detection software will be able to aggregate experience across thousands of patients and suggest additional diagnostic options to the physician. Combining insights from these types of analyses with more traditional analytics should help providers to standardize diagnoses and assist radiologists, increase efficiency and reduce costs.

 

Such uses can go beyond imaging to include nanotechnology sensors embedded a pill or ingested fluids. These sensors can contribute to the data used to create recommended diagnoses and treatment plans.

 

How will AI affect the GCHEG membership?

 

AI will enable the delivery personalized, proactive healthcare that is more efficient, more effective and less expensive. In order for it to work properly, AI is best enabled in a unified analytic structure that has already learned to govern and manage data within the organization. This structure accompanied by the right blend of software, clinicians and data scientists can make AI a reality for improving healthcare. Just as the EMR required major focus and development of infrastructure, AI will take some work to realize its potential.

 

How can we begin to prepare for AI?

 

·       Take time to become more “data savvy” by learning about ways others are using analytics to improve care.

·       Support efforts of your organization to better manage data and turn it in to useful information.

·       Offer refresher training in basic statistics and data management, so no one is left behind.

·       Make sure you are using software that not only handles day-to-day reporting needs but is able to scale for the future.

·       Consider adding to your team a data scientist, who can develop data models and provide guidance in practical use of advanced techniques.

 

Interested in other perspectives? Consider reading “9 predictions on how artificial intelligence will change clinical care – for the better or worse?” by Laura Dyrda of Becker’s Hospital Review. The article includes insights from me and several other industry experts.

 

This should get you started and better prepared to take advantage of AI and other new technologies that are on the horizon. Healthcare can surely benefit from smarter use of data!

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