Reinventing Clinical Decision Support

Paul Cerrato, MA, and John Halamka, MD, MS
JUNE 15, 2018

Knowledge- and Non-Knowledge based CDS Tools

While clinical decision support technology will not eliminate such biases, it challenges clinicians to think twice before jumping to conclusions by presenting them with other diagnostic possibilities that don’t readily come to mind. These digital tools come in a variety of types but can be divided into 2 broad categories: Knowledge-based and non-knowledge based. The former is made up of a database that includes scientific details on specific diseases, the best options for treatment, etc. The CDS tool typically includes decision trees with “If/Then” statements to guide the diagnostic process based on data from each patient’s medical record.

Non-knowledge based CDS systems rely on artificial intelligence (AI), machine learning, and neural networks to detect hidden patterns in patient data. Neural networks are software constructs that mimic the neurons and synapses found in the human brain. The artificial neurons, called nodes, are linked together in a network that can analyze input from medical images, for example, which in turn helps differentiate pathology from normal tissue. These deep-learning programs review thousands or millions of images, separating normal from abnormal findings. During the training phase, these programs make many mistakes, so they use a protocol called back propagation to review these miscalculations and gradually correct them, learning from its own errors without a programmer adding new code to the software.

UpToDate, available from Wolters Kluwer, is a good example of a knowledge-based CDS tool. This large online service is the equivalent of a massive medical textbook that covers numerous specialties—with an important difference: Textbooks are typically revised every 5 years, while the database is updated every few months. The company recently released a more sophisticated version of its service called UpToDate Advanced, which includes interactive algorithms that let clinicians take a more personalized approach to patient care. The advanced version of UpToDate also includes a tool to help providers interpret abnormal lab results. (pictured right courtesy Wolters Kluwer.)

VisualDx, another CDS system, provides point-of-care advice in primary care, emergency medicine, dermatology, and hospital medicine, with a strong emphasis on visual depictions of pathology. Unlike UpToDate, VisualDx includes a symptom and sign finder to help clinicians build a customized differential diagnosis. For instance, if the patient presents with anemia but also has seizures, the CDS tool will walk the user through a specific pathway, while a patient with anemia and hypotension will prompt the system to show a separate pathway and a different potential diagnosis. When the anemia/seizure option is chosen, VisualDx then presents the user with a list of possibilities, including chronic kidney disease, hemolytic uremic syndrome, and so on, using graphics and labels to walk the clinician through the next step in the diagnostic process. Including links to relevant articles to describe the condition, as well as ICD codes, drug reaction data, and proper tests to perform. It even provides links UpToDate and PubMed for clinicians who want to dive deeper.

ClinicalKey, like UpToDate, is a searchable database that addresses the needs of clinicians in a wide variety of specialties and in primary care practice. It relies on content from thousands of biomedical journals published by Elsevier, the company that makes ClinicalKey. One of the useful components of the database is its “smart” search function. As you begin to type a term into the search box, it lists numerous alternatives you may not have considered. For instance, as you begin typing the word diabetes, the service provides a drop-down menu that includes terms like diabetes-related complication, diabetic retinopathy, and diabetic ketoacidosis. Like several other CDS tools, ClinicalKey lets users integrate the service into an EHR system.

Elsevier also offers STATdx for Radiology, which contains thousands of images and complex diagnoses, helping specialists compare their patients’ finding to confirmed diagnoses of specific disorders. ExpertPath for Pathology is a similar service for pathologists.

While these CDS systems are impressive, they only scratch the surface. In recent years, researchers have demonstrated that machine-learning-enabled software can take the diagnostic process into previously uncharted territory. For example, Google scientists, working in conjunction with researchers at the University of Texas, Austin, used machine-learning algorithms to scan retinal images of patients suspected of having diabetic complications. When they compared computer-generated diagnoses of diabetic retinopathy to that performed by 54 experienced ophthalmologists and senior residents, they found the computer was more accurate than its human counterparts at detecting the disorder.

Researchers have also demonstrated that it’s possible to use neural networks that scan millions of skin lesions to differentiate between a normal mole and a malignant melanoma.

Implementing the latest CDS Tools

Although studies like these often generate headlines, commercially available CDS systems that take full advantage of AI are not quite ready for prime time. But that should not stop healthcare organizations from taking advantage of the sophisticated tools already available to the medical community. For instance, the SHRINE software mentioned above is freely available as open source, so it can be used by other medical centers to establish similar data sharing networks. In fact, several organizations have taken advantage of this opportunity. A project called SHRINE National, for instance, linked 8 medical centers to study co-existing disorders related to diabetes and autism. (Details on how to implement SHRINE are available here.)

Commercially available CDS systems can be plugged into a medical practice or hospital in several ways. UpToDate, for instance, offers single-user subscription options for medical professionals, residents, fellows, students, and patients. Multiuser subscriptions are available for hospitals, institutions, and group practices. Since the service is compatible with several EHR systems, it is also possible to fold the program into the same computer network that houses one’s patient records, reducing the need to log out of one electronic system and into another. Other vendors have similar implementation options, with pricing varying depending on specialty, the number of users, and whether an advanced informational option is chosen.

There’s little doubt that the advanced clinical decision support tools used to guide Kathy Halamka’s treatment have been invaluable. The individualized chemotherapy regimen minimized the impact of Taxol on her fine motor skills, her partial mastectomy was a success, and her osteopenia—an adverse effect of chemotherapy—has decreased. She continues to live an active, full life running an animal sanctuary in a Boston suburb. And as CDS tools like ClinicalQuery saturate the medical profession, it’s likely that the misdiagnosis statistics and their ugly effects will gradually shrink.
 

Paul Cerrato has more than 30 years of experience working in healthcare as a clinician, educator, and medical editor. He has written extensively on clinical medicine, electronic health records, protected health information security, practice management, and clinical decision support. He has served as editor of Information Week Healthcare, executive editor of Contemporary OB/GYN, senior editor of RN Magazine, and contributing writer/editor for the Yale University School of Medicine, the American Academy of Pediatrics, Information Week, Medscape, Healthcare Finance News, IMedicalapps.com, and Medpage Today. The Healthcare Information and Management Systems Society (HIMSS) has listed Mr. Cerrato as one of the most influential columnists in healthcare IT.


John Halamka, MD, MS, is the international healthcare innovation professor at Harvard Medical School, chief information officer of the Beth Israel Deaconess System, and a practicing emergency physician. He strives to improve healthcare quality, safety, and efficiency for patients, providers, and payers throughout the world using information technology. He has written 5 books, several hundred articles, and the popular Geekdoctor blog.





 

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