Modeling Decisions for Artificial Intelligence
Buy eBook. Buy Hardcover. Buy Softcover. Rent the eBook. The second step is to develop a strategy for changing the focus of medical education to empower physicians to maintain oversight of AI. Physicians, nurses, and experts in the field of safe hospital communication must control the transition to AI integrated care because there is significant risk during the transition period and much of this risk is subtle, unique to the hospital environment, and outside the expertise of AI designers.
AI is needed in acute care because AI detects complex relational time-series patterns within datasets and this level of analysis transcends conventional threshold based analysis applied in hospital protocols in use today.
For this reason medical education will have to change to provide healthcare workers with the ability to understand and over-read relational time pattern centered communications from AI. Medical education will need to place less emphasis on threshold decision making and a greater focus on detection, analysis, and the pathophysiologic basis of relational time patterns.
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Effective communication between human and artificial intelligence requires a common pattern centered knowledge base. Experts in safety focused human to human communication in hospitals should lead during this transition process. Asclepius was credited with such a powerful intellect that he altered the ratio of living to dead.
While mortal physicians have never matched his success, for thousands of years patients have placed their confidence in the intellect of physicians for medical diagnosis and care. However, the present role of physicians and nurses as preeminent diagnosticians and providers of care may soon be overtaken by computers. While many argue that this transition is inevitable, physicians have not yet developed a formal plan to respond to the challenge from Silicon Valley.
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This is a momentous time and the process of developing a formal plan to prevent the need to pass the Caduceus should not be further delayed. The first step in the prevention of loss of the position of physicians and nurses as preeminent overseers of hospital care is to understand the present limitations of medical diagnostics in the acute care environment and in medical education which have driven the need for AI integration. A computer programmer examining the present threshold based hospital protocols with the intent to develop algorithms for managing care might quickly conclude that automation of acute care diagnostics and treatment would be easy to implement.
The reason for this is that present hospital protocols are based on twentieth century threshold decision making [ 1 ] and are generally quite simple. In an example, it might appear to a computer programmer that all that is required to diagnose and treat sepsis is an indication that infection is suspected, a simple threshold breach detection algorithm [ 2 ] and a branching set of treatment rules.
However expert clinicians know that these simple protocols are not indicative of the true levels of acute care complexity [ 3 , 4 ]. The randomized controlled trials RCT which use the threshold rules applied in hospital protocols as unified standards for an entire population are subject to marked heterogeneous treatment effects HTE [ 5 ].
Such trials provide evidence of the average treatment effect on the group under test as a whole but not whether the treatment used in the RCT will be beneficial or harmful to the instant patient under care. It therefore logically follows that no protocol, no matter how well supported by RCT, can be applied without expert oversight provided by either by a human or AI to protect patients from harm. For this reason designers of automated systems must recognize that the true complexity of diagnosis and management of adverse conditions, such as sepsis, resides in the portion of diagnosis and care provided by nuanced expert oversight which is difficult to study and reproduce.
In the alternative the expert physician knows to detect and track worsening, knows the potential for HTE, and modifies care off protocol if necessary.
Physicians and nurses can provide oversight because the decision process of the protocols are transparent so, using their own intellect, they are able to modify the diagnosis and treatment as needed in real-time. The present state of nuanced threshold decision making. The future of medical AI extends far beyond the interpretation of medical images, pathology slides and radiographs. AI is being developed to detect critical, highly complex and time dependent conditions such as adverse drug reactions and sepsis [ 6 , 7 ] in acute care environments where timely nuanced communication is pivotal.
However, a major disadvantage is that the complex decision processing of the AI may be substantially opaque if not designed to provide transparency and nuanced communication. Effective communication from an AI must not be inferior to communication from a human. However the need for detailed real-time communication is even more acute when the adverse clinical conditions under investigation and care are highly complex and rapidly progressing critical conditions. For this reason, the introduction of AI into the acute care environment should be preceded by detailed consideration of how AI, if not properly implemented, may cause harm by adversely affecting communication as well as the role of the physician and nurse as expert overseers of care.
In the worst case, complex analytics may be provided by AI without disclosure of the data used to make the decision or the analytic processes applied. This approach is reasonable in acute care only if expert human oversight will not be beneficial even if the patient is worsening. This limited output would constitute a communication error by human standards because it does not communicate the relational time patterns RTP of laboratory tests and vitals detected, the other factors considered, how the patterns were combined, which component patterns are rapidly worsening, and how the overall state of the sepsis pattern has progressed over time for example in relation to treatment or to a potentially inciting event such as a surgery.webmail.lauren.reclaim.hosting/map84.php
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This level of communication would be required if the detection was performed by a human expert so the AI must deliver equivalent or better communication. While black box AI is clearly unacceptable in acute care, the standards for AI transparency and for timely communication by AI of the details of its decision process have not been determined. Yet it is clear that AI should meet the minimum standards of detail required for physician to physician handoff.
Oversight by the expert is not facilitated because she cannot see the pattern combinations detected by the AI. Complex decisions made by AI without transparency or detailed timely communication of the factors considered and how these factors were combined to render decision process. If the patient worsens under the care of the AI the handoff to the physician will be blind. One problem with AI use in acute care which may not be readily apparent to those with a limited understanding of the medical domain is the common delay between the decision and the result which occurs responsive to the decision.
Contrast this to AI based autonomous driving. Here, a human in the car can rapidly intervene because the correctness of the decisions made by the AI are generally immediately transparent to the overseeing driver. The immediacy of feedback simplifies the training processes of AI based autonomous driving and allows the use of black box AI because the decision process is largely irrelevant and can be opaque to the driver who is only concerned with the result which is promptly apparent.
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By comparison, in medical care, where the correctness of the decision is often not immediately obvious, the clinician responsible for the patient cannot simply wait with faith to see the outcome. Rather the clinician needs to be able to see the decision making process itself in fine relational detail and in real-time before the outcome to be sure it applies to the complexities and comorbidities of the instant patient under care.
Here we can easily see the need for a new focus of medical education because bedside transparency of AI is not useful if the physician is not trained to be able to interpret the RTP detected by the AI. The addition of AI brings new communication challenges to a complex hospital environment already associated with a high error rate.
Yet any action which moves care from one caregiver to another is a handoff. This is true even if a computer is the diagnostician and is now handing off the care to a human who is expected to deliver treatment. Guidelines for handoffs involving AI should be prioritized to assure they are ready when AI is integrated into hospital care. In addition the danger of the potential delay between decision and result will likely be most evident when a patient managed by Black Box AI fails to recover. The clinician will want to know if a mistake was made in AI based diagnoses or in the therapeutic choices made or both.
Here the need for real-time and detailed communication is evident. The clinician must know which components of the data set comprised the basis for the AI decision and what part of the decision process might be wrong. The answers to these pivotal questions will not be evident when detailed real time communication is lacking and the physician is blind to processing. Again this shows the need for refocused education since transparency and disclosure of RTP detected by the AI are only useful to the physician if she is trained to interpret them.
Fortunately there is already a major body of literature directed toward the study of factors which induce error during human to human communication in the patient care environment. Under these guidelines, a physician handing off care to another must provide detailed communication which explains the time patterns or threshold breaches identified in the data, the diagnostic considerations, and the state and rational for various therapies applied or under consideration.
With those lessons learned, it follows that an AI system which is entrusted with critical clinical decisions, should be considered a human equivalent for the purpose of defining AI communication protocols and standards.
The second problem which may develop over time in the acute care environment comprises intellectual dependency on AI, especially AI which lacks detailed communication capability. The lack of detailed communication renders the state presented in Fig. As the perceived need to learn complex pathophysiology diminishes, so too does the competency of the physician or nurse. Perhaps there will come a time when AI is so highly effective that loss of the quality of the oversight function of nurses and physicians will not be a concern.
However, over the next few decades we must focus on the transition state. This is the present state of automated driving, where the performance of AI must still be evaluated and supervised by a human in real time. There is little doubt that there will be a diminishing role for the portions of traditional medical education which are focused on simple rules and threshold decision making. Also, physicians will not be able to rely on those simple threshold rules to over read the outputs of the AI because the weakness of those rules is one of the reasons AI is being introduced.
With the emergence of AI, simple threshold based diagnostic and treatment rules will be replaced by AI based protocols which will be much more complex and will include RTP analysis. Fortunately the teaching of threshold rules, which became popular in the late twentieth century, was always an oversimplification so the abandonment of this aspect of medical education will not be much of a loss. Careers for graduates: learn about Solution 49 and our human-centred design approach.
Through our focus on decision-making, we help our clients deliver their customer engagement and operational strategies. We do this by conceiving and implementing intelligent systems, architectures, processes and new operating models. Our work can involve embedding artificial intelligence, machine-based learning, cognitive computing, advanced analytics, probabilistic reasoning and deterministic business rules management into core processes and functions within client organisations.
Our team includes a broad spectrum of skills from strategists to statisticians, scientists to software developers, anthropologists to behavioural economists.
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The team also engage socially and many are friends outside of work and participate in events together like Oxfam Walk, The Colour Run, Tough Mudder, the list goes on. During the day your work ranges from creating information strategies and solution designs, to creating proposals for clients, engaging client stakeholders, implementing software, writing analytics code and developing cognitive computing solutions.
At the end of the day you may discuss emerging technologies with your colleagues on the way to a Data Science Meetup. Finally, you head home to spend time with your family and friends.
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