More so than any other billion-dollar industry in the United States, every facet of healthcare is inextricably tied to individual people and their individual circumstances. Patients seeking treatment, providers offering their specialties, and the communities and organizations supporting them all rely on intelligent interpersonal communication and reasoning to fulfill the unique needs of each entity in the enormous healthcare network.
This poses a unique opportunity for artificial intelligence (AI) and machine learning (ML) to add value to healthcare delivery. The aim of AI is to incorporate human-like reasoning skills into computer systems, and ML is the evolving ability of computers to discover patterns in data without prior human insight. Whether these two related tools are used in isolation or in conjunction, there are incredible opportunities for AI / ML to alleviate demands on human labor. While AI / ML provides numerous avenues to improve business critical operations across all industries, AI / ML in healthcare delivery can play an elevated role in saving and rehabilitating lives.
Benefits Found in Medical Research
As medical research continues to advance prophylactic, diagnostic, and treatment opportunities, the expectation that healthcare providers deliver individualized care rises. While these advances address the full range of potential ailments, they also introduce a burden on healthcare providers.
Healthcare professionals are exposed to a variety of severe occupational stressors, such as time pressure, low social support at work, high workloads, uncertainty concerning patient treatment, and predisposition to emotional responses due to exposure to suffering and dying patients. [Source]
As available options increase, the decision-making process becomes more challenging, putting pressure on healthcare providers.
Particularly, in the past 35 years, the prevalence of stress-related illnesses such as burnout has increased significantly, affecting 19–30% of employees in the general working population globally. Burnout among healthcare workers, mainly medical staff, was becoming an occupational hazard, with its rate reaching between 25% and 75% in some clinical specialties. [Source]
Doctor Performance Benefits
While it may first appear that the burnout issue is limited to issues of job satisfaction or employee turnover, addressing it is of critical importance in the healthcare. Recent research has found that burnout – across all specialties – leads to a 50% increase in medical errors and that “clinicians with burnout are more likely to subjectively rate patient safety lower in their organizations and to admit to having made mistakes or delivered substandard care at work.” And just as these healthcare providers are looking to minimize risk, in the United States, the third leading cause of death is unintentional accidents, which includes both medical mistakes and drug overdoses (among other causes). Any method for lightening the load on professionals in the healthcare industry must be considered. AI / ML can be used to lighten workloads and allow medical professionals to perform the critical aspects of their jobs that rely on their expertise.
Even when healthcare specialists are abundant, the workload can be overwhelming. For example, given the immense value of medical imaging in diagnoses and treatment, calls for medical imaging are made faster than radiologists can read results. ML algorithms can act as a first pass on imaging data, helping determine positive or negative results. The radiologists would primarily review only positive results, giving them more time with each image that crosses their desk and provide more detailed findings. As with many AI / ML use cases, this example highlights how these modern tools can work in conjunction with professionals: AI / ML algorithms often perform as well as radiologists in detecting abnormalities, yet the humans are superior in differentiating the exact diagnoses. The providers outperform the models, yet these models still have a role.
Improved Medical Records
AI / ML can also unburden healthcare in routine healthcare settings. When a physician enters a current symptom into a medical record, previous instances of that symptom could be brought to their attention, giving a better understanding of the history of the condition even when the patient may not know to bring to their physicians’ attention. Using NLP tools, these symptoms can be entered into the system any kind of format: handwritten notes, voice memos, or entered as fields in software – it wouldn’t matter for future retrieval with the help of AI. Furthermore, items in the patient’s history that could indicate comorbidities, bad drug interactions, drug addiction risk, life-threatening allergies, and family history could be parsed and shared with both the healthcare provider and the patient can be brought into a treatment plan without stressing the recall of the parties involved. Overall, it would allow providers to assess entire medical histories without having to complete the impossible task of physically reading through entire lifetimes of medical histories.
AI / ML can also help as a sanity check. Healthcare providers balance numerous concerns for their patients in their mind and make a decision as to the best course of action. When a doctor orders a drug for a patient, for example, an AI / ML integrated system can instantaneously alert the physician of areas of concern in the patient’s history. The physician may see they hadn’t considered a drug interaction that would cause more harm than good and, in that moment, recognize another approach is preferable. Conversely, the physician may have already taken the content of the alert into account when making their decision and veto the system’s suggestion for another approach. Even in overriding the machine recommendation, this alert could help the patient-physician team develop a pre-emptive plan in anticipation of potential future issues, building trust in their relationship.
Integrating AI / ML tools could vastly lighten the load of professionals throughout the healthcare system. These advantages are particularly impactful in healthcare, where human-to-human interfacing is often rushed and overwhelmed given the demand for treatment. The use cases discussed above demonstrate that incorporating these powerful tools into healthcare delivery is possible, and as these innovative technologies have been commercialized, the path to enterprise AI has become attainable for all healthcare organizations.