Dataset Information

EMBalance - validation of a decision support system in the early diagnostic evaluation and management plan formulation of balance disorders in primary care: study protocol of a feasibility randomised controlled trial.

ABSTRACT: Balance problems are caused by multiple factors and often lead to falls and related fractures, bringing large socio-economic costs. The complexity of balance control mechanisms, the lack of medical expertise, and the absence of specialised equipment contribute to the delayed or incorrect diagnosis and management ofthese patients. Advances in computer science have allowed the development of computer systems that support clinical diagnosis and treatment decisions based on individualised patient data. The aim of the EMBalance decision support system (DSS) is to support doctors facing this clinical challenge, to make a definitive diagnosis and implement an effective management plan. The EMBalance study will determine the accuracy of this supportive tool when used by non-specialist doctors. This study is funded by the European Union's Seventh Framework Programme.EMBalance is a proof-of-concept study designed as a non-commercial, international, multi-centre, single-blind, parallel-group randomised controlled trial to be carried out at four clinical sites in the United Kingdom, Germany, Greece and Belgium. The study is comprised of three stages: internal pilot, phase I (diagnosis) and stage II (management). For this purpose, 200 patients presenting with persistent dizziness (>3 months' duration) to primary care services will be randomised to either the intervention group (diagnostic assessment with the DSS) or a control group (diagnostic assessment without the DSS). Patients allocated to the intervention group will be assessed by a doctor with the support of the EMBalance DSS, while patients allocated to the control group will receive a visit as per standard practice. Ultimately, all patients' diagnoses and management plans will be certified by a consultant in neuro-otology.EMBalance is the first trial to test the accuracy of a DSS in both the diagnosis of and the management plan for vestibular disorders across the healthcare systems of four different countries. The EMBalance study is the result of a combined effort of engineers and physicians to develop an accurate tool to support non-specialist doctors, with no risk for the patient. This trial will provide reliable information about the benefits of implementing DSSs in primary care while supporting the feasibility of testing the EMBalance algorithms in further research.ClinicalTrials.gov NCT02704819 . Registered 29 February 2016.

SUBMITTER: Rammazzo L

PROVIDER: S-EPMC5011840 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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EMBalance - validation of a decision support system in the early diagnostic evaluation and management plan formulation of balance disorders in primary care: study protocol of a feasibility randomised controlled trial.

Rammazzo Laura L Kikidis Dimitris D Anwer Amal A Macdonald Nora N Kyrodimos Efthymios E Maurer Christoph C Wuyts Floris F Luxon Linda L Bibas Athanasios A Bamiou Doris-Eva DE

Trials 20160905 1

<h4>Background</h4>Balance problems are caused by multiple factors and often lead to falls and related fractures, bringing large socio-economic costs. The complexity of balance control mechanisms, the lack of medical expertise, and the absence of specialised equipment contribute to the delayed or incorrect diagnosis and management ofthese patients. Advances in computer science have allowed the development of computer systems that support clinical diagnosis and treatment decisions based on indivi ...[more]

PMID: 27596486

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Project description:BackgroundAlthough cancer survivorship care plans have been in use for several years, they have been shown to not be effective in meeting the long-term needs of cancer survivors, in addition being generic and passive in nature. Interactive survivorship care plans in the form of a personal decision support aid could provide an opportunity to not only engage survivors in their health care, but also capture meaningful treatment-related outcomes to use as a rich data source as the basis for making informed decisions. The objective of this research is to formulate an evidence-based model framework for implementing breast cancer survivorship guidelines via an online breast cancer survivorship care plan (SCP).MethodsThe study was completed in three steps. In the first step, or the requirements gathering phase, we conducted personal interviews of breast cancer survivors to determine their use of the survivorship care plan (SCP) and related needs to determine core SCP functions and formulate an implementation framework for an online SCP. In the second step, we used the framework as a guide to design and develop the online SCP tool. Finally, in the third step, we conducted preliminary testing to determine the feasibility of the developed tool among online users.ResultsFifteen breast cancer survivors were consulted, who reported several issues from their use of the traditional paper-based SCP. Four themes were identified that represent the SCP's core desired functions. Eight features were matched to implement these core functions. Using a personal decision approach, an online SCP tool called ACESO that incorporates these features and functions was developed. Preliminary feasibility testing yielded overall positive responses from breast cancer survivors (n = 51).ConclusionOur study demonstrated that survivors face challenges from their use of a traditional paper-based SCP. The online SCP we developed is technically feasible and has the potential to effectively engage breast cancer survivors in self-management and shared decision-making with their clinicians and caregivers. Further testing is required to assess its usability and long-term impact.

Project description:BackgroundDiagnostic errors pose significant health risks and contribute to patient mortality. With the growing accessibility of electronic health records, machine learning models offer a promising avenue for enhancing diagnosis quality. Current research has primarily focused on a limited set of diseases with ample training data, neglecting diagnostic scenarios with limited data availability.ObjectiveThis study aims to develop an information retrieval (IR)-based framework that accommodates data sparsity to facilitate broader diagnostic decision support.MethodsWe introduced an IR-based diagnostic decision support framework called CliniqIR. It uses clinical text records, the Unified Medical Language System Metathesaurus, and 33 million PubMed abstracts to classify a broad spectrum of diagnoses independent of training data availability. CliniqIR is designed to be compatible with any IR framework. Therefore, we implemented it using both dense and sparse retrieval approaches. We compared CliniqIR's performance to that of pretrained clinical transformer models such as Clinical Bidirectional Encoder Representations from Transformers (ClinicalBERT) in supervised and zero-shot settings. Subsequently, we combined the strength of supervised fine-tuned ClinicalBERT and CliniqIR to build an ensemble framework that delivers state-of-the-art diagnostic predictions.ResultsOn a complex diagnosis data set (DC3) without any training data, CliniqIR models returned the correct diagnosis within their top 3 predictions. On the Medical Information Mart for Intensive Care III data set, CliniqIR models surpassed ClinicalBERT in predicting diagnoses with <5 training samples by an average difference in mean reciprocal rank of 0.10. In a zero-shot setting where models received no disease-specific training, CliniqIR still outperformed the pretrained transformer models with a greater mean reciprocal rank of at least 0.10. Furthermore, in most conditions, our ensemble framework surpassed the performance of its individual components, demonstrating its enhanced ability to make precise diagnostic predictions.ConclusionsOur experiments highlight the importance of IR in leveraging unstructured knowledge resources to identify infrequently encountered diagnoses. In addition, our ensemble framework benefits from combining the complementary strengths of the supervised and retrieval-based models to diagnose a broad spectrum of diseases.

Project description:BACKGROUND:The number of trained specialists world-wide is insufficient to serve all children with pediatric rheumatologic disorders, even in the countries with robust medical resources. We evaluated the potential of diagnostic decision support software (DDSS) to alleviate this shortage by assessing the ability of such software to improve the diagnostic accuracy of non-specialists. METHODS:Using vignettes of actual clinical cases, clinician testers generated a differential diagnosis before and after using diagnostic decision support software. The evaluation used the SimulConsult® DDSS tool, based on Bayesian pattern matching with temporal onset of each finding in each disease. The tool covered 5405 diseases (averaging 22 findings per disease). Rheumatology content in the database was developed using both primary references and textbooks. The frequency, timing, age of onset and age of disappearance of findings, as well as their incidence, treatability, and heritability were taken into account in order to guide diagnostic decision making. These capabilities allowed key information such as pertinent negatives and evolution over time to be used in the computations. Efficacy was measured by comparing whether the correct condition was included in the differential diagnosis generated by clinicians before using the software ("unaided"), versus after use of the DDSS ("aided"). RESULTS:The 26 clinicians demonstrated a significant reduction in diagnostic errors following introduction of the software, from 28% errors while unaided to 15% using decision support (p?<?0.0001). Improvement was greatest for emergency medicine physicians (p?=?0.013) and clinicians in practice for less than 10 years (p?=?0.012). This error reduction occurred despite the fact that testers employed an "open book" approach to generate their initial lists of potential diagnoses, spending an average of 8.6 min using printed and electronic sources of medical information before using the diagnostic software. CONCLUSIONS:These findings suggest that decision support can reduce diagnostic errors and improve use of relevant information by generalists. Such assistance could potentially help relieve the shortage of experts in pediatric rheumatology and similarly underserved specialties by improving generalists' ability to evaluate and diagnose patients presenting with musculoskeletal complaints. TRIAL REGISTRATION:ClinicalTrials.gov ID: NCT02205086.

Project description:AimsThe aim of this feasibility trial was to evaluate the feasibility and acceptability of the locally adapted Group Problem Management Plus (PM+) intervention for women in the conflict affected settings in Swat, Pakistan.MethodsThis mixed-methods study incorporated a quantitative component consisting of a two arm cluster randomised controlled feasibility trial, and qualitative evaluation of the acceptability of the Group PM+ to a range of stakeholder groups. For the quantitative component, on average from each of the 20 Lady Health Workers (LHWs) catchment area (20 clusters), six women were screened and recruited for the trial with score of >2 on the General Health Questionnaire and score of >16 on the WHO Disability Assessment Schedule. These LHW clusters were randomised on a 1 : 1 allocation ratio using a computer-based software through a simple randomisation method to the Group PM+ intervention or Enhanced Usual Care. The Group PM+ intervention consisted of five weekly sessions of 2 h duration delivered by local non-specialist females under supervision. The primary outcome was individual psychological distress, measured by levels of anxiety and depression on the Hospital Anxiety and Depression Scale at 7th week after baseline. Secondary outcomes include symptoms of depression, post-traumatic stress disorder (PTSD), general psychological profile, levels of functioning and generalised psychological distress. Intervention acceptability was explored through in-depth interviews.ResultsThe results show that lay-helpers with no prior mental health experience can be trained to achieve the desired competency to successfully deliver the intervention in community settings under supervision. There was a good intervention uptake, with Group PM+ considered useful by participants, their families and lay-helpers. The outcome evaluation, which was not based on a large enough study to identify statistically significant results, indicated statistically significant improvements in depression, anxiety, general psychological profile and functioning. The PTSD symptoms and depressive disorder scores showed a trend in favour of the intervention.ConclusionThis trial showed robust acceptance in the local settings with delivery by non-specialists under supervision by local trained females. The trial paves the way for further adaptation and exploration of the outcomes through larger-scale implementation and definitive randomised controlled trials in the local settings.

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Dataset Information

EMBalance - validation of a decision support system in the early diagnostic evaluation and management plan formulation of balance disorders in primary care: study protocol of a feasibility randomised controlled trial.

Publications

EMBalance - validation of a decision support system in the early diagnostic evaluation and management plan formulation of balance disorders in primary care: study protocol of a feasibility randomised controlled trial.

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