The socio-organizational age of artificial intelligence in medicine

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Abstract

The increasing pressure on Health Care Organizations (HCOs) to ensure efficiency and cost-effectiveness, balancing quality of care and cost containment, will drive them towards a more effective management of medical knowledge derived from research findings. The relation between science and health services has until recently been too casual. The primary job of medical research has been to understand the mechanisms of disease and produce new treatments, not to worry about the effectiveness of the new treatments or their implementation. As a result many new treatments have taken years to become part of routine practice, ineffective treatments have been widely used, and medicine has been opinion rather than evidence based. This results in suboptimal care for patients. Knowledge management technology may provide effective approaches in speeding up the diffusion of innovative medical procedures whose clinical effectiveness have been proved: the most interesting one is represented by computer-based utilization of evidence-based clinical guidelines. As researchers in Artificial Intelligence in Medicine (AIM), we are committed to foster the strategic transition from opinion to evidence-based decision making. Reviews of the effectiveness of various methods of guideline dissemination show that the most predictable impact is achieved when the guideline is made accessible through computer-based and patient specific reminders that are integrated into the clinician’s workflow. However, the traditional single doctor–patient relationship is being replaced by one in which the patient is managed by a team of health care professionals, each specializing in one aspect of care. Such shared care depends critically on the ability to share patient-specific information and medical knowledge easily among them. Strategically there is a need to take a more clinical process view of health care delivery and to identify the appropriate organizational and information infrastructures to support this process. Thus, the great challenge for AIM researchers is to exploit the astonishing capabilities of new technologies to disseminate their tools to benefit HCOs by assuring the conditions of knowledge management and organizational learning at the fullest extent possible. To achieve such a strategic goal, a guideline can be viewed as a model of the care process. It must be combined with an organization model of the specific HCO to build patient careflow management systems. Artificial intelligence can be extensively used to design innovative tools to support all the development stages of those systems. However, exploiting the knowledge represented in a guideline to build them requires to extend today’s workflow technology by solving some challenging problems.

Introduction

The increasing pressure on health care organizations (HCOs) to ensure efficiency and cost-effectiveness, balancing quality of care and cost containment, will drive them towards a more effective management of medical knowledge derived from research findings. There is a general appreciation that clinical decisions must be based on evidence to a much greater degree than they have been in the past. They should be made by combining three factors; evidence, values, and resources. However, many health care decisions are still based principally on values and pay little attention to evidence derived from research, the scientific factor, and to resources, the socio-economic factor. This will change: as the pressure on those factors increases, decisions will have to be made and justified explicitly and publicly.

There are unacceptable delays in the implementation of many findings of research [5]. This results in suboptimal care for patients. Collections of systematic reviews and critical appraisals of primary research are valued knowledge sources [6]. However, their proliferation is creating its own information explosion. Knowledge management technology may provide effective approaches in speeding up the diffusion of innovative medical procedures whose clinical effectiveness have been proved: the most interesting one is represented by computer-based utilization of evidence-based clinical guidelines [1], [2], [3], [4], [7]. As researchers in artificial intelligence in medicine (AIM), we are committed to foster the strategic transition from opinion-based to evidence-based decision making.

As defined by the Institute of Medicine, clinical guidelines are “systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances” [8]. Clinicians, policy makers, and payers see guidelines as a tool to reduce variability in practice, control costs, and improve patient care outcomes.

However, the development of good guidelines does not ensure their use in practice. Therefore, to maximize the likelihood of a clinical guideline being used we need coherent dissemination and implementation strategies to capitalize on known positive factors and to deal with obstacles to implementation that have already been identified [9]. Reviews of the effectiveness of various methods of guideline dissemination show that the most predictable impact is achieved when the guideline is made accessible through computer-based and patient specific reminders that are integrated into the clinician’s workflow.

A further dimension of the problem of guideline dissemination in HCOs needs to be considered. For its understanding we must start from the fact that the single doctor–patient relationship is being replaced by one in which the patient is managed by a team of health care professionals, each specializing in one aspect of care. Such shared care depends critically on the ability to share patient-specific information and medical knowledge easily among care providers. Indeed it is the present inability to share guidelines across systems and organizations that represents one of the major impediments to progress towards an evidence-based care. Strategically there is a need to take a more clinical process view of health care delivery and to identify the appropriate organizational and information infrastructures to support this process.

To achieve such a strategic goal, a guideline can be viewed as a model of the care process. It must be combined with an organization model of the specific HCO to build a patient careflow management system (CfMS). Artificial intelligence (AI) can be extensively used to design innovative tools to support all the development stages of a CfMS. However, exploiting the knowledge represented in a guideline to build a CfMS requires that we extend today’s workflow technology by solving some challenging problems. In contrast with most industrial or office processes, medical processes may often be unpredictable, because of the intrinsic uncertainty and complexity present in most of the patient management phases. As a matter of fact, even if a guideline illustrates the steps to follow in pre-defined situations, it may happen either that a new, unpredictable situation arises, or the physician, that is the final decision-maker, is not always compliant with the guideline. Thus, a CfMS must be flexible enough to handle sudden modifications of the pre-defined plan, and to truly support health care professionals in their work rather than overly constrain them.

Another peculiarity of HCOs is that medical professionals are not normally situated in front of a computer. The latter is a more typical situation for administrative office operators. Thus, a CfMS relying on simple message delivery among workstations is not suitable. It is essential to find modalities for sending messages able to reach the operators as soon as possible, but with particular attention not to burden them excessively. That is to say that the system must have knowledge about the urgency of the tasks, according to the patient condition, in order to choose the best modality for advising the operator. Mobile and wireless network technology promises great possibilities in this respect.

Section snippets

Knowledge-based health care organizations

Knowledge has come to be recognized and handled as a valuable entity in itself. It has been called the ultimate intangible. There are some estimates that intellectual capital now comprises typically 75–80% of the total balance sheet of companies. Today, knowledge is a key enterprise asset [10]. This is particularly true in HCOs where the typical business is knowledge-based since they are composed largely by specialists who direct and discipline their own performance through organized feedback

Knowledge creation

In the area of knowledge management, it has been pointed out that a large part of knowledge is not explicit but tacit. Following Polanyi’s [12] epistemological investigation, tacit knowledge is characterized by the fact that it is personal, context specific, and therefore hard to formalize and communicate. Explicit, on the other hand, is the knowledge that is transmittable through any systematic language. Polanyi contends that human beings acquire knowledge by actively creating and organizing

Decision making

Decisions are commitments by individuals or organizations to actions which are justified in relation to goals and objectives, and are predicated on information and beliefs about how the selected strategies and the actions selected will lead to desired outcomes. At least two important models of decision making process need to be considered: the rational model and the process model. The rational model, developed initially by Simon [21], conceptualizes decision making as goal directed and problem

Learning organizations

Although learning is something undertaken and developed by individuals, organizations can foster or inhibit the process. The organizational culture within which individuals work shapes their engagement with the learning process. Organizations that position learning as a core characteristic have been termed “learning organizations” and this concept is a fundamental one in the context of organization development. It is important to note the difference between the terms learning organization and

Knowledge representation

Building learning HCOs requires the effective exploitation of new ICT for the management of knowledge, expertise and skills from three different domains: medical, organizational, and technological [33].

By medical domain knowledge we mean any representation of medical work as a set of activities that result from the interaction between patients and heath care professionals. Professionals do their work in an organizational setting: their work depends on the material and financial resources made

Patient careflow management systems

While clinical guidelines describe the activities of a medical team in a comprehensive manner for the purpose of defining the best practice for patients’ management, patient careflows (Cfs) focus on the behavioral aspects of the medical work with regard to a possible support of their execution through advanced ICT. It specializes the concept of workflow in the clinical domain. A workflow is an activity involving the coordinated execution of multiple tasks performed by different agents [47].

Conclusions

The view of an organization as a machine for “information processing” is deeply ingrained in the traditions of management. According to this view, the only useful knowledge is formal and systematic — quantifiable data, codified procedures, universal principles — and the key metrics for measuring the value of new knowledge are similarly hard and quantifiable. But there is another more recent way to think about knowledge and its role in organizations: creating new knowledge is not simply a matter

Acknowledgements

This work has been partially funded by the European Commission through the project M2DM (Multi-access services for Telematic Management of Diabetes Mellitus) within the Telematics Applications Programme and by MURST through the Project No. 9809224324 (Una Architettura Basata su Agenti a Supporto del Lavoro Cooperativo in Medicina).

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