A principled approach to the design of healthcare systems: Autonomy vs. governance

Abstract

In this paper, we look at decision support for post-operative breast cancer care. Our main concerns are to support autonomy of decision making whilst maintaining the governance and reliability of the decision-making process. We describe the context of our work in the wider medical setting. We then present a set of decision support tools based on the situation calculus as a means of maintaining the integrity of rule bases underlying the decision-making system. The decision support system, Neptune, allows for the authoring, maintenance and delivery of decisions in a self-governing framework. Finally we discuss the implications of our work.

Introduction

This project on decision support for post-operative breast cancer care [1] raises a number of interdisciplinary questions in a complex and emotive area. The project is collaboration between computer scientists, statisticians and clinicians, a complex arrangement in itself. The nature of the subject involves the making of difficult decisions as clinicians and patients are faced with life and death choices. The decision support processes must therefore follow methods that promote the safety and reliability of the outputted decision whilst also maintaining system robustness and fault tolerance. Additionally from an HCI viewpoint we are also faced with supporting and not supplanting clinician's judgments. There are also wider issues arising from the implications of our studies regarding the historical clinical data and what that might mean for future approaches to prognosis.

In an earlier paper [2], an approach to process understanding, in breast cancer care, was discussed. It described how decisions on post-operative breast cancer treatment are currently governed by a set of medical guidelines including the National Institute for Clinical Evidence (NICE) guidelines [3] in which the decision process is as follows (Fig. 1): The clinician uses available data with a staging method to define the patient risk category. The risk category is then used to determine the type of treatment (T1…T6) that the patient could be offered. Medical experts are expected to make the best available decision in the circumstances. However, this becomes increasingly difficult as available medical knowledge expands. Computers and decision support system software have the capacity to handle and deal with all this information. In general computers are better than humans at managing large amounts of information and the resulting complexity. Thus, the guidelines approach has the potential to make medical practice more consistent, efficacious, safer and cost effective [4]. However, guidelines can cause benefit or harm and need to be “…rigorously developed, evidence based guidelines [to] minimise the potential harms”. Additionally the uptake by the clinicians needs to be addressed as, in practice; guidelines are often not used [5].

The evidence-based approach to the delivery of medical care has gained wide recognition within the healthcare community, advocating that decision making should use current knowledge and clinical evidence from systematic research [6]. In breast cancer care, in particular, there are currently a number of staging methods widely used by clinicians, in particular the Nottingham and Manchester staging systems. However, there is no standard method to support clinicians’ decision-making processes as to how and when to include new evidence, and how to validate emerging local patient data patterns or other models and practices from elsewhere. This means that there is no standard way to ensure that clinicians are following accepted guidelines or deviating from them. There may be valid clinical reasons why a standard decision path is not chosen (e.g. the age or infirmity of the patient) but these decisions are not currently recorded in a standard way. In comparison computer implemented guidelines have the potential to be better used at a local level by:

• improving accessibility by the clinical team,

• performing additional tasks such as prescription writing,

• warning of potential hazards,

• and providing alternative views of the information.

On a larger scale; systems can take on functions such as: recommendation, presentation, documentation, registration, communication, explanation, calculation and aggregation.

There are many contributory factors to the complexity of designing these systems, some of which are described below:

• Environment and context—The aims of national initiatives such as NPFit/PACIT [7], [8] in driving down costs and errors, etc. The complexity of the context depends on the observer. So the views of the various reforming agencies almost certainly differ from those of the practitioners.

• The autonomy of clinicians versus the governance requirements of clinical audit and traceability to support for instance clinical governance. In any case the autonomy of action is bounded within set limits. This is a complex relationship that must be represented in any up to date implemented system.

• Resource limitations—staffing, drugs, radiology, etc. Again resources are bounded.

• The limitations of medical knowledge and how that knowledge evolves. So it must be a priority to ensure that new knowledge can quickly and easily be assimilated into the system without stopping and restarting.

• The co-evolution of knowledge and the needs for system validation and safety. So the system needs to evolve in a safe, reliable and predictable manner as prescribed by the governance strictures.

• The requirements for safe solutions and effective treatment, as part of the overall goal of engineering a reliable safety critical system.

• The (moving) requirements for knowledge elicitation to be used to update the decision processes.

• The ability of computer scientists to encode medical knowledge is limited to expert advice gained from clinicians.

• The limitations of data mining approaches as a means to supporting evidence-based, decision making.

Finally, there is the concern over the impact of the introduction of computer support in the decision process and the effect this may have on clinical practice.

In both the UK and US there are national initiatives to introduce greater use of IT in clinical settings. The broad aims of the NPFit and PACIT programmes are similar. They aim to streamline data processing to cut costs and reduce clinical errors. For example, it is proposed that electronic prescribing of medicines will cut costs in paperwork and reduce prescribing errors which account for a large number of patient deaths (44,000–98,000 deaths caused by all types of medical errors in the USA). Both schemes aim to introduce electronic patient records, again to cut costs of paper records and reduce errors from paper-based systems. Both systems also look to more clinical governance and audit of medical processes so that medical staff are more accountable for their actions. The UK initiative is already displaying the signs of a large project out of control with the projected costs of £6Bn rising to between £18Bn and £31Bn. The lack of user centred design is evident by a recent [9] poll showing 75% of family doctors are not certain that NPFit will ever meets its goals. The first stage of the electronic appointment systems has largely failed to meets its use targets. However, a smaller scale introduction of region-wide IT in the Wirral was more widely accepted with 90% of family surgeries and the vast number of patients accepting the system. Thus IT systems can succeed. This is important for our work, for in order to succeed, it requires a working IT health infrastructure. Furthermore the twin goals of cost and error reduction may be mutually incompatible. As Reason points out [10] organisations have processes for productivity and safety but circumstances will arise, either through unsafe acts or latent system weaknesses, which lead to organisational failure. Safety protocols may be violated in the name of efficiency or sets of latent weaknesses will line up to cause an accident. Many individual errors are the result of cognitive under-specification [11] of the user's tasks. In this project we aim to over-specify and support clinical tasks by first describing both the decision and system control processes and representing them as agent-based deliberative architectures in the situation calculus. This leads to a natural implementation in a custom designed language, Neptune that implements the situation calculus expressions within a grid based architecture called clouds. This will provide a robust means of supporting decision making and ensuring that chances to decisions protocols remain valid. Some of the sources of complexity in the design of healthcare systems are addressed. More specifically, we are interested in safe and reliable decision support for post-operative breast cancer care and the wider lessons we can learn from our experiences. Accordingly, the next Section 2 provides an overview of related work in the area of support for clinical guidelines. Section 3 then gives an overview of the formal methodology used in our approach. Whilst Section 4 shows the development process from system description to formal model to the actual implementation. This approach is evaluated in Section 5 with conclusions and future work closing the paper in Section 6.