It is argued here that the knowledge domain relevant to point-of-care devices lies with integrating a level of decision support or associated software on them; however, limited evidence was found supporting this happening on devices, though, DSSs already exist in health care for health professionals. Literature also demonstrated the use of decision support tools for health professionals although a caveat is given that they must not be used in isolation, rather as a guide. Literature demonstrating decision support tools for patients was limited but it will be an emerging area in the future since examples were seen of patients being trained in how to interpret their results for both asthma and diabetes management.
2.4.4.1 Decision support for health professionals
From the literature reviewed, no expert systems were found existing on devices; however semi-automated DSSs existed in which the user had to make a judgement about the suggestions proposed. Health professionals could play a part in helping to develop advanced automated DSSs. Their input would be vital in limiting unnecessary errors that may be introduced through computer programs. Where fully-
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automated systems cannot be used, dedicated health professionals could be devoted to analysing the data.
Ethical issues also require consideration, as automated decision support from devices, if inaccurate, may cause a less optimal course of action to be taken, which may ultimately affect a patient‘s well-being.
From the decision-making process in arriving at a diagnosis, as described by Sullivan and Wyatt (2005b), the diagram in Figure 2-3 was constructed as a generic decision-making process. It describes the steps taken to make an informed decision. The patient presented their symptoms and the doctor observed other indications during assessment of the patient. Both of these are considered before arriving at a possible diagnosis. The process of reaching a diagnosis can either be by hypo- deductive reasoning or inductive reasoning depending on the level of experience of the doctor.
A possible diagnosis enables the doctor to continue with the appropriate procedure to treat the patient; however a differential diagnosis (i.e. other diagnoses which may also result from the symptoms presented) may also be made at this time.
Subsequently, the patient may be further examined and/or medical tests may be carried out to confirm the illness. This results in a particular action or treatment being administered to the patient. Sometimes multiple conditions present similar symptoms; hence the initial diagnosis may be incorrect thus resulting in a loop where another possible diagnosis is given. Incorporating a part of this process into a computerised DSS may aid in speeding up the overall decision-making process.
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Figure 2-3 – Example of a decision-making process by the health professional
2.4.4.2 Decision support for patients
In the case of providing a computerised decision support tool for patients using point- of-care devices, the same principle used when doctors make a diagnosis may be applied. A glucometer is used as an example. Currently, their mode of operation is they provide blood glucose results to the user. If decision support could be incorporated on the device it could take the following form.
Into the glucometer would be incorporated a range of expected values that have been predefined and agreed by both patient and health professional. The user performs the test which is recorded and stored in the glucometer. Depending on whether the result meets the requirements will then determine the subsequent steps. If a result within the given range is seen, there is no need for the patient to take an action. However, if an extreme or out of range result is obtained, a set of questions could be posed to the patient to help ascertain the cause of such a result. The predefined result range could be combined with a DSS algorithm and possible suggestions of how the patient could improve their result would then be displayed.
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Figure 2-4 is a flow chart of the steps which would be required in such a process. To date, such a system does not exist on glucometers. The closest to it, involves the tagging or annotation of results to allow further discussion of results when the patient visits their health professional.
User performs blood glucose test No action is needed If result is within expected range
Pose questions to user to help ascertain cause of result
Proposed suggestions of how to improve result
If result falls outside expected range Predefined range of expected values DSS algorithm +
Figure 2-4 – A proposal of on-device decision support for patients
Although a description has been given of a possible method of incorporating decision support on devices, evidence from literature suggested that data was transferred to external software to provide information to the patient. It is therefore suggested that in order for decision support to be fully functional, it could only occur away from the device.
67 2.4.4.3 Features of a decision support system
A DSS, whether simplistic or complex, is suggested for all the devices mentioned in this paper as it would help to provide initial suggestions to aid users in determining the most appropriate diagnosis and as a result, the optimal therapy can be administered. Decision support could also play a role in the external software used to manipulate the data as it could help enhance decisions made by health professionals.
The approaches seen in the literature to support users making appropriate decisions were as follows: The functionality to download data from point-of-care devices to external software so that it could be further manipulated. This facility existed in glucometers, spirometers and blood gas analyzers; providing access to results via a web portal; educating and training users in how to interpret results. This is a way of empowering patients to enable them to manage their health consistently.
2.4.4.4 Factors limiting decision support on point-of-care devices
One hypothesis for the limitation of on-device information display may be that existing point-of-care devices are limited in the computational technology they can incorporate. Mobile phones and PDAs are small devices with advanced technology, as they are portable and can have sophisticated functionalities; hence their usage as data transfer aids for point-of-care devices could be incorporated into the overall system for decision support. However regulatory boards exist (FDA [USA] and MHRA (Medicines and Healthcare products Regulatory Agency [UK])) of which approval is required before new medical devices can be released for general usage. There may also be an economical reason if the state-of-the-art technology has a potential to be expensive, it may not be affordable to users. Biehl and Velten (2008) proposed lab-on-a-chip (LOC) technology for POCT devices due to their compact size. However the challenges that must be overcome are the cost of development and approval from governing bodies.
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