Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Determinants of household costs and access to care for tuberculosis in Tajikistan

This thesis investigated access to medical care for tuberculosis (TB). TB is among the worst infectious diseases globally, causing about 2 million deaths per year. TB control will not be possible, if the most vulnerable do not have access to appropriate treatment. Studies for the present thesis were carried out in Tajikistan, one of the poorest countries of the world. The health system of Tajikistan is firmly rooted in its Soviet history with a dense network of facilities and allegiances to separate vertical programs for different tasks of the health system. The seven studies in this thesis used qualitative and quantitative methods to investigate access to care. The first study, using focus groups, found that community members, TB patients and health care providers all considered economic factors the most important barriers to medical care. A subsequent study investigated delay to TB treatment. Generally moderate delays (median 52 days) until start of TB treatment were found. However, two subgroups of patients had long health system delays. These were the patients who first presented to peripheral primary care facilities and especially those who developed active disease while working in Russia. The long delays of the former are related to the vertical structure of TB control inherited from Soviet times: primary care providers were reluctant to diagnose TB. Diagnosis at the primary care level based on sputum smear microscopy should be promoted to shorten the delays of these patients. For labour migrants developing active TB in Russia, an international referral system is needed, including availability of treatment until sputum conversion for Tajik citizens in Russia. The third study investigated illness-related costs at the level of the patients’ households. Mean self-reported total costs of an episode of TB were USD1’053, or c. USD4’900 purchasing power parity. The costs peaked before starting TB treatment and in the intensive phase of TB treatment. Hence, the costs of an episode of TB are catastrophic and both strategies to reduce costs and strategies to help patients cope with costs are urgently needed. These strategies should be timed early in treatment in order to correspond with the highest cost peak. The fourth study identified factors associated with higher expenditure for TB. It further investigated coping strategies that may lead to impoverishment: selling productive assets and borrowing money. Receiving ‘additional medication’ predicted higher direct costs. Further significant predictors were the delay until start of TB treatment and hospitalisation. TB patients raised on average USD182 through selling productive assets and through borrowing. Based on the results, it was suggested that ‘additional treatment’ should be diminished to reduce costs for patients. The potentially detrimental coping strategies employed confirm the severe economic burden that TB patients carry. The fifth study used data from the TB registry to identify predictors of hospitalisation and positive treatment outcomes. Sputum smear result was the most important predictor of hospitalisation, with age and sex being further significant factors. Treatment success was significantly lower for sputum-smear positive patients and there was a tendency for lower treatment success among hospitalised patients. It is recommended that national guidelines be adapted to emphasise outpatient treatment. A survey among patients found that a considerable proportion of TB patients had already received the three food supplements that they are entitled to – before the end of the treatment. Food supplements made a contribution of about USD225 to the household economy. Bayesian modelling of the sensitivity of routine sputum smear microscopy in peripheral laboratories in Tajikistan yielded an estimate of sensitivity of 53% for the examination of a single slide. The contribution of the third slide to total case finding through sputum microscopy was estimated at 13%. These results suggest that the third serial sputum specimen could make a substantial contribution to case finding, if it were carried out with equal quality as previous examinations. The sensitivity of routine sputum microscopy in the studied districts is reasonably good and its use should be promoted. Concurrently, strengthening of the quality assurance should continue. The present thesis found that an analytical framework for access to care, developed in the context of a malaria control program, is useful also in the area of TB. Adaptations to make the analytical framework fit better to the context of access to TB care are suggested. This thesis identified economic factors as the main barriers to access medical care for TB. Several characteristics of health care delivery rooted in the Soviet health system contributed to the high costs faced by patients and to the long delays until treatment experienced by certain subgroups of patients. The importance of factors related to the Soviet history of health care suggests that many of our findings may also apply to other post-Soviet countries. In order to improve access to TB care and hence TB control in Tajikistan, the economic burden for the patients must be reduced as a matter of priority. Further, the long delays of certain subgroups of patients need to be shortened. The latter can be achieved more easily than the former, among others by improving referral systems and by promoting the use of sputum smear microscopy. Reducing the economic burden for TB patients requires measures on both sides: reducing the costs faced by patients and increasing their ability to cope with these costs. Collaboration between the health system, non-governmental organisations and funding agencies as well as between different programs within the health system, like the TB control program and primary care, will be necessary

Audit of Antenatal Testing of Sexually Transmissible Infections and Blood Borne Viruses at Western Australian Hospitals

In August 2007, the Western Australian Department of Health (DOH) released updated recommendations for testing of sexually transmissible infections (STI) and blood-borne viruses (BBV) in antenates. Prior to this, the Royal Australian & New Zealand College of Obstetricians & Gynaecologists (RANZCOG) antenatal testing recommendations had been accepted practice in most antenatal settings. The RANZCOG recommends that testing for HIV, syphilis, hepatitis B and C be offered at the first antenatal visit. The DOH recommends that in addition, chlamydia testing be offered. We conducted a baseline audit of antenatal STI/BBV testing in women who delivered at selected public hospitals before the DOH recommendations. We examined the medical records of 200 women who had delivered before 1st July 2007 from each of the sevenWAhospitals included in the audit. STI and BBV testing information and demographic data were collected. Of the 1,409 women included, 1,205 (86%) were non-Aboriginal and 200 (14%) were Aboriginal. High proportions of women had been tested for HIV (76%), syphilis (86%), hepatitis C (87%) and hepatitis B (88%). Overall, 72% of women had undergone STI/BBV testing in accordance with RANZCOG recommendations. However, chlamydia testing was evident in only 18% of records. STI/BBV prevalence ranged from 3.9% (CI 1.5– 6.3%) for chlamydia, to 1.7% (CI 1–2.4%) for hepatitis C, 0.7% (CI 0.3–1.2) for hepatitis B and 0.6% (CI 0.2–1) for syphilis. Prior to the DOH recommendations, nearly three-quarters of antenates had undergone STI/BBV testing in accordance with RANZCOG recommendations, but less than one fifth had been tested for chlamydia. The DOH recommendations will be further promoted with the assistance of hospitals and other stakeholders. A future audit will be conducted to determine the proportion of women tested according to the DOH recommendations. The hand book from this conference is available for download Published in 2008 by the Australasian Society for HIV Medicine Inc © Australasian Society for HIV Medicine Inc 2008 ISBN: 978-1-920773-59-

An integrated modelling approach for R5-X4 mutation and HAART therapy assessment

We have modelled the within-patient evolutionary process during HIV infection using different methodologies. New viral strains arise during the course of HIV infection. These multiple strains of the virus are able to use different coreceptors, in particular the CCR5 and the CXCR4 (R5 and X4 phenotypes, respectively)influence the progression of the disease to the AIDS phase. We present a model of HIV early infection and CTLs response which describes the dynamics of R5 quasispecies, specifying the R5 to X4 switch and effects of immune response. We illustrate dynamics of HIV multiple strains in the presence of multidrug HAART therapy. The HAART combined with X4 strain blocker drugs might help to reduce infectivity and lead to slower progression of disease. On the methodology side, our model represents a paradigm of integrating formal methods and mathematical models as a general framework to study HIV multiple strains during disease progression, and will inch towards providing help in selecting among vaccines and drug therapies. The results presented here are one of the rare cases of methodological cross comparison (stochastic and deterministic) and a novel implementation of model checking in therapy validation

Decision-Support for Rheumatoid Arthritis Using Bayesian Networks: Diagnosis, Management, and Personalised Care.

PhD Theses.Bayesian networks (BNs) have been widely proposed for medical decision support. One advantage of a BN is reasoning under uncertainty, which is pervasive in medicine. Another advantage is that a BN can be built from both data and knowledge and so can be applied in circumstances where a complete dataset is not available. In this thesis, we examine how BNs can be used for the decision support challenges of chronic diseases. As a case study, we study Rheumatoid Arthritis (RA), which is a chronic inflammatory disease causing swollen and painful joints. The work has been done as part of a collaborative project including clinicians from Barts and the London NHS Trust involved in the treatment of RA. The work covers three stages of decision support, with progressively less available data. The first decision support stage is diagnosis. Various criteria have been proposed by clinicians for early diagnosis but these criteria are deterministic and so do not capture diagnostic uncertainty, which is a concern for patients with mild symptoms in the early stages of the disease. We address this problem by building a BN model for diagnosing RA. The diagnostic BN model is built using both a dataset of 360 patients provided by the clinicians and their knowledge as experts in this domain. The choice of factors to include in the diagnostic model is informed by knowledge, including a model of the care pathway which shows what information is available for diagnosis. Knowledge is used to classify the factors as risk factors, relevant comorbidities, evidence of pathogenesis mechanism, signs, symptoms, and serology results, so that the structure of BN model matches the clinical understanding of RA. Since most of the factors are present in the dataset, we are able to train the parameters of the diagnostic BN from the data. This diagnostic BN model obtains promising results in differentiating RA cases from other inflammatory arthritis cases. Aware that eliciting knowledge is time-consuming and could limit the uptake of these techniques, we consider two alternative approaches. First, we compare its diagnostic performance with an alternative BN model entirely learnt from data; we argue that having a clinically meaningful structure allows us to explain clinical scenarios in a way that cannot be done with the model learnt purely from data. We also examine whether useful knowledge can be retrieved from existing vi medical ontologies, such as SNOMED CT and UMLS. Preliminary results show that it could be feasible to use such sources to partially automate knowledge collection. After patients have been diagnosed with RA, they are monitored regularly by a clinical team until the activity of their disease becomes low. The typical care arrangement has two challenges: first, regular meetings with clinicians occur infrequently at fixed intervals (e.g., every six months), during which time the activity of the disease can increase (or ‘flare’) and decrease several times. Secondly, the best medications or combinations of medications must be found for each patient, but changes can only be made when the patient visits the clinic. We therefore develop this stage of decision support in two parts: the first and simplest part looks at how the frequency of clinic appointments could be varied; the second part builds on this to support decisions to adjust medication dosage. We describe this as the ‘self-management’ decision support model. Disease activity is commonly measured with Disease Activity Score 28 (DAS28). Since the joint count parts of this can be assessed by the patient, the possibility of collecting regular (e.g., weekly) DAS28 data has been proposed. It is not yet in wide use, perhaps because of the overheads to the clinical team of reviewing data regularly. The dataset available to us for this work came from a feasibility study conducted by the clinical collaborators of one system for collecting data from patients, although the frequency is only quarterly. The aim of the ‘self-management’ decision support system is therefore to sit between patient-entered data and the clinical team, saving the work of clinically assessing all the data. Specifically, in the first part we wish to predict disease activity so that an appointment should be made sooner, distinguishing this from patients whose disease is well-managed so that the interval between appointments can be increased. To achieve this, we build a dynamic BN (DBN) model to monitor disease activity and to indicate to patients and their clinicians whether a clinical review is needed. We use the data and a set of dummy patient scenarios designed by the experts to evaluate the performance of the DBN. The second part of the ‘self-management’ decision support stage extends the DBN to give advice on adjustments to the medication dosage. This is of particular clinical interest since one class of medications used (biological disease-modifying antirheumatic drugs) are very expensive and, although effective at reducing disease activity, can have severe adverse reactions. For both these reasons, decision support that allowed a patient to ‘taper’ the dosage of medications without frequent clinic visits would be very useful. This extension does not meet all the decision support needs, which ideally would also cover decision-making about the choice of medications. However, we have found that as yet there is neither sufficient data nor knowledge for this. vii The third and final stage of decision support is targeted at patients who live with RA. RA can have profound impacts on the quality of life (QoL) of those who live with it, affecting work, financial status, friendships, and relationships. Information from patient organisations such as the leaflets prepared by the National Rheumatoid Arthritis Society (NRAS) contains advice on managing QoL, but the advice is generic, leaving it up to each patient to select the advice most relevant to their specific circumstances. Our aim is therefore to build a BN-based decision support system to personalise the recommendations for enhancing the QoL of RA patients. We have built a BN to infer three components of QoL (independence, participation, and empowerment) and shown how this can be used to target advice. Since there is no data, the BN is developed from expert knowledge and literature. To evaluate the resulting system, including the BN, we use a set of patient interviews conducted and coded by our collaborators. The recommendations of the system were compared with those of experts in a set of test scenarios created from the interviews; the comparison shows promising results

Mobile Health Technologies

Mobile Health Technologies, also known as mHealth technologies, have emerged, amongst healthcare providers, as the ultimate Technologies-of-Choice for the 21st century in delivering not only transformative change in healthcare delivery, but also critical health information to different communities of practice in integrated healthcare information systems. mHealth technologies nurture seamless platforms and pragmatic tools for managing pertinent health information across the continuum of different healthcare providers. mHealth technologies commonly utilize mobile medical devices, monitoring and wireless devices, and/or telemedicine in healthcare delivery and health research. Today, mHealth technologies provide opportunities to record and monitor conditions of patients with chronic diseases such as asthma, Chronic Obstructive Pulmonary Diseases (COPD) and diabetes mellitus. The intent of this book is to enlighten readers about the theories and applications of mHealth technologies in the healthcare domain