Data Pulse

A Brief Tour of Artificial Intelligence in Healthcare

Matthew Marcetich

• Verlag: New Degree Press
• Jahr: 2020
• ISBN:
  9781641375405
• Auflage: 1
• Thema: Computer Science
• Thema: Artificial Intelligence (AI) & Semantics

For many of us, machine learning and artificial intelligence (AI) are abstract terms that have become popularized for their roles in automation and robotics. In healthcare, uses of AI emerged several decades ago and have significantly expanded to the present day.

Data Pulse presents a current snapshot of uses of AI in healthcare, including essential opportunities and challenges. The discussion explores its impact at many levels of the healthcare system, from researchers and entrepreneurs to physicians and patients.

With easily understood language, Marcetich defines common terms of AI and shows us how various AI tools are influencing research, clinical, and administrative areas of healthcare. The reader will learn how current discoveries build on the decades of previous work in biology, robotics, and computer science, along with the unforeseen ethical and legal challenges they have provoked.

Data Pulse is a story of important partnerships and strategies that are reshaping modern healthcare through AI. It will inform our view of the past, present, and rapidly evolving future of AI in healthcare.

Part 1:

Introduction
to the Basics

Chapter 0:
Introduction

The speed with which health IT achieves its full potential depends far less on the technology than on whether its key stakeholders—government officials, technology vendors and innovators, health care administrators, physicians, training leaders, and patients—work together and make wise choices.

—Robert Wachter, physician and author of The Digital Doctor1

In the United States, nearly anyone who interacts with the health care system generates data. Your health data are generated in hospital administrative systems as soon as you check in for your appointment. Your data are generated and stored in electronic health record (EHR) systems, which began as billing systems and have grown to include lab data and research data. Health data are stored in population health records and used by state health departments to understand health trends and monitor disease spread. Even finance databases contain health data. After all, your spending habits can tell a story, albeit a partial one, of factors that could be affecting your health.

EHR data represent traditional health data: use and sharing of the data are regulated and protected, the data are derived directly from patient-physician encounters, and the data represent a combination of clinical details, behavioral patterns, research, and prescription drug and billing information. These data are generated during routine clinical care and emergency visits; the data are protected by the Health Insurance Portability and Accountability Act (HIPAA), which outlines a set of national standards for the protection of certain health information. Increasingly, health data are generated in nontraditional ways, in situations outside of physician-patient interactions. An employee wellness application collects health data, and thousands of health apps are available to collect nutrition and fitness data, to monitor blood sugar for diabetic children from afar, or to guide mothers through breastfeeding, as a few examples. If you have a smartphone, chances are your device is monitoring your heart rate or the number of steps you walked today.

Whether the data are traditional or nontraditional, widespread agreement among physicians and patients, and a bit of common sense and intuition, suggests that health data are highly personal. Traditional health data are also highly confidential while the confidentiality of nontraditional data is blurry, since the nontraditional health data are not legally protected. Depending on privacy agreements, the nontraditional health data could be sold or shared with third parties.

Especially for traditional health data, much effort is focused on keeping the data secure and confidential. Data are stored on encrypted databases, transmission of those data are monitored and encrypted (at rest and in transit), and the personal devices used to access health data (i.e., cell phones, computers) are ideally—but not always—designated for the sole purpose of handling health data. Some medical institutions take extra precautions by evaluating the data integrity risks of any device that touches health data and requiring those devices to be monitored.

Such protections lead to administrative burdens and ethical dilemmas. The incoming medical student wonders: Should I set up email forwarding from my hospital email account to my personal email for convenience? The clinical data scientist, who is checking their email on a Friday afternoon from a local coffee shop, wonders: Is it okay to use the public Wi-Fi on my work computer, even if I’m not emailing any health data? Under data trust policies governing traditional health data, casual access to and sharing of health data are forbidden, and academic medical centers are increasingly providing analytical tools that reside behind institutional firewalls to allow staff and students to develop analytical tools in protected virtual environments. Use of virtual private networks (VPNs) are becoming mandatory when connecting to public Wi-Fi networks; at some institutions, the VPN will evaluate a foreign Wi-Fi network for potential vulnerabilities before establishing a connection.

In the United States, nurses, medical doctors, and pharmacists are ranked as the most trusted occupations.2 They earn trust by relating and listening to the patient, by adhering to privacy and ethical standards, and through an inherent responsibility to help the patient through medical circumstances ranging from benign to catastrophic. As use and governance of health data permeate the continuum of health care delivery, the role of technical occupations, such as computer engineers and data scientists, will become increasingly important. And while these professions are not outlined in the list of most trusted occupations, probably because of their hidden role in health care, perhaps one day they will be included.

During the course of medical care, data are generated in a multitude of ways. Let’s take a look at a few possible scenarios:

•Scenario 1: The patient walks into the pharmacy with a sore throat, approaches the pharmacist, who is helping another patient while placing a physician on hold, and describes her symptoms. The pharmacist finishes his phone call and then takes a throat swab of the patient and performs a test for Group A streptococcus bacteria. He gets results within minutes. Laws in some states, including Ohio, are changing to allow pharmacists to perform or order clinical tests.3 If the test is negative, the pharmacist reassures and directs the patient to aisle seven for over-the-counter cold medication, cautioning that the patient should see a physician if the symptoms persist, and especially if she begins to develop a fever. If the test is positive, the pharmacist advises the patient to see a nurse practitioner or physician right away for appropriate treatment.

•Scenario 2: During a scheduled yearly physical exam, the physician introduces herself to the patient as she sits down across from the patient while logging into her clinic’s desktop computer. She mentions that the clinic uses an EHR system to record data from patient visits, which allows the patient to set up an account to schedule another visit, view lab results, and check their prescriptions. During the course of the physical exam, the physician receives an electronic reminder to screen for hepatitis C based on CDC recommendations. The patient decides to enroll to receive electronic notifications from the EHR system. In a few moments, the patient receives an email with a link to the system’s secure portal. The next day, the patient receives a phone call from his physician, who notifies him that he tested positive for hepatitis C, a serious but treatable condition. During the phone call, the physician outlines a course of treatment and mentions that treatment details will be provided in the EHR system. (Note: if possible, the physician will ask to see the patient in person to discuss treatment options although telemedicine is increasingly used to facilitate virtual consultations.) After logging in to the EHR, the patient sees his test results and a reassuring note from his physician. Given the patient’s insurance and the lab results, his physician suggests a regimen of glecaprevir for eight weeks followed by sofosbuvir for twelve weeks. The physician scheduled follow-up visits and lab work, which are viewable in a calendar in the patient’s health record portal.

•Scenario 3: The patient, with a routine history of inflammatory bowel disease, logs in to her personal health account to review her doctor’s instructions for her upcoming colonoscopy. The patient receives reminders multiple days in advance of the appointment that describe how to prepare for the colonoscopy. She follows instructions outlined in the reminders. After checking in to the clinic for the colonoscopy, the doctor asks the patient if she prepared for the p...