Without surprise, artificial intelligence continues to be of critical focus across the digital health ecosystem. HIMSS Media conducted a survey on artificial intelligence and machine learning, with results confirming this. Responses indicated widespread optimism about the application of artificial intelligence in healthcare settings, particularly in the treatment of chronic conditions. Of the respondents, 77 percent said they are already using artificial intelligence to support clinical decisions.
With chronic conditions increasing in prevalence, respondents see AI and machine learning holding major transformative potential for improving care, with more than half of respondents citing conditions like cancer, heart disease and diabetes as priority areas for leveraging these innovations. 33 percent of respondents were already crafting strategies for AI and machine-learning within their organizations.
Like any exciting new form of health innovation, determining how it will address clinical needs should occur before implementation occurs to ensure the best strategy, as Claus Duedal Pedersen, Denmark Hospital and Health Care touches on in the following HIMSS TV clip.
A HIMSS Insights eBook explores the clinical side of emerging AI technologies, such as triage chatbots. This aspect of AI could combat a challenge that clinicians are all too familiar with – patients seeking answers and potentially becoming misinformed about their conditions when searching their symptoms. In a patient-facing chatbot, patients can input their symptoms, answer questions about them and then have medically supported information and resources offer guidance in a curated response.
Another aspect of artificial intelligence in healthcare that shows immense promise for transforming the clinician experience is natural language processing (NLP).
“[NLP] technology enables computers to derive computable and actionable data from text, especially when text is recorded in the form of natural human language (i.e., phrases, sentences, paragraphs). This technology allows humans to record information in the most natural method of human communication (narrative text), and then enables computers to extract actionable information from that text. NLP is also capable of analyzing the often non-standard grammatical constructions common in medical lingo. Natural language understanding (NLU) is a subset of NLP that uses reasoning, inference and semantic searching to help clinicians make decisions and take action.”
What’s most exciting about NLP technology is that it records information in the form of natural human language and then enables computers to extract actionable information from that text. This makes possible the inclusion of a patient’s narrative, a central component of medical records.
“The [HIMSS] Health Story Project was founded on the premise that a central component of medical records is the narrative, textual information that conveys the patient’s unique story as well as the clinician’s thoughts and rationale for treatment decisions. Check boxes and drop-down lists simply cannot capture these nuances,” said Laura Bryan, MS, CHDS, AHDI-F, vice president, MedEDocs and HIMSS Health Story Project co-chair. “When the record consists of only canned responses, all patients with diabetes, for example, end up looking the same on paper. User interfaces that limit a clinician to record information about the patient’s story using check boxes and drop-down lists is not cake. It is a problem that creates burden and dissatisfaction for physicians who are forced to work in a way that is natural for computers, not humans.”
So how can NLP alleviate this challenge? One way is by ultimately bridging the divide between narrative and the ability to machine process and encode important concept, said Bryan. “NLP makes unstructured data relevant and actionable. It derives contextual meaning from the clinician’s textual notes and unlocks critical portions of the record for both clinical and administrative uses.”
It will take time for artificial intelligence in healthcare to prove its promise, but based on our research and the applications we’re currently exploring, the promise of these innovations will be worth it.
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Originally published January 9, 2019, updated May 31, 2019