Dataset Information

Development of a transparent interactive decision interrogator to facilitate the decision-making process in health care.

ABSTRACT:

Background

Decisions about the use of new technologies in health care are often based on complex economic models. Decision makers frequently make informal judgments about evidence, uncertainty, and the assumptions that underpin these models.

Objectives

Transparent interactive decision interrogator (TIDI) facilitates more formal critique of decision models by decision makers such as members of appraisal committees of the National Institute for Health and Clinical Excellence in the UK. By allowing them to run advanced statistical models under different scenarios in real time, TIDI can make the decision process more efficient and transparent, while avoiding limitations on pre-prepared analysis.

Methods

TIDI, programmed in Visual Basic for applications within Excel, provides an interface for controlling all components of a decision model developed in the appropriate software (e.g., meta-analysis in WinBUGS and the decision model in R) by linking software packages using RExcel and R2WinBUGS. TIDI's graphical controls allow the user to modify assumptions and to run the decision model, and results are returned to an Excel spreadsheet. A tool displaying tornado plots helps to evaluate the influence of individual parameters on the model outcomes, and an interactive meta-analysis module allows the user to select any combination of available studies, explore the impact of bias adjustment, and view results using forest plots. We demonstrate TIDI using an example of a decision model in antenatal care.

Conclusion

Use of TIDI during the NICE appraisal of tumor necrosis factor-alpha inhibitors (in psoriatic arthritis) successfully demonstrated its ability to facilitate critiques of the decision models by decision makers.

SUBMITTER: Bujkiewicz S

PROVIDER: S-EPMC3161376 | biostudies-literature | 2011 Jul-Aug

REPOSITORIES: biostudies-literature

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Publications

Development of a transparent interactive decision interrogator to facilitate the decision-making process in health care.

Bujkiewicz Sylwia S Jones Hayley E HE Lai Monica C W MC Cooper Nicola J NJ Hawkins Neil N Squires Hazel H Abrams Keith R KR Spiegelhalter David J DJ Sutton Alex J AJ

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research 20110430 5

<h4>Background</h4>Decisions about the use of new technologies in health care are often based on complex economic models. Decision makers frequently make informal judgments about evidence, uncertainty, and the assumptions that underpin these models.<h4>Objectives</h4>Transparent interactive decision interrogator (TIDI) facilitates more formal critique of decision models by decision makers such as members of appraisal committees of the National Institute for Health and Clinical Excellence in the ...[more]

PMID: 21839417

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Project description:AIM:The aim of this study was to explore shared decision-making among residents, their families and staff to determine relevant strategies to support shared decision-making in long-term care (LTC). BACKGROUND:Meaningful engagement of long-term care home (LTCH) residents and their families in care decisions is key in the provision of quality of care. Shared decision-making is an interprofessional approach to increasing resident and family engagement in care decisions which can lead to higher quality decisions, more relevant care interventions and greater resident, family, and staff satisfaction. Despite these advantages, shared decision-making has not been widely implemented in practice in LTC. METHODS:The study took place in one LTCH in Toronto, Ontario, Canada. A qualitative descriptive design was used to explore how residents, family members and staff described how they collaborate when making decisions concerning resident care, and their perceptions of facilitators and challenges to a collaborative approach to decision-making. Individual interviews were conducted with nine participants: residents, families and staff. Data were analysed using content and thematic analysis. FINDINGS:Four main themes that described resident, family and staff perspectives of shared decision-making were as follows: (a) oral communication pathways for information sharing; (b) supporting resident decision-making autonomy; (c) relational aspects of care facilitate shared decision-making; and (d) lack of effective communication creates barriers to shared decision-making. CONCLUSION:As the demand for LTC continues to increase, it is crucial that healthcare providers engage in collaborative, relational practices that foster high-quality resident care. While a relational approach to care can facilitate shared decision-making, there are opportunities to further cultivate shared decision-making in LTCHs through more effective communication and collaboration. IMPLICATIONS FOR PRACTICE:Understanding how information is shared and decisions are made can facilitate shared decision-making in LTCHs. The strategies identified from this study could be further co-developed and implemented in LTCHs.

Project description:BackgroundThe amount of available textual health data such as scientific and biomedical literature is constantly growing and becoming more and more challenging for health professionals to properly summarize those data and practice evidence-based clinical decision making. Moreover, the exploration of unstructured health text data is challenging for professionals without computer science knowledge due to limited time, resources, and skills. Current tools to explore text data lack ease of use, require high computational efforts, and incorporate domain knowledge and focus on topics of interest with difficulty.ObjectiveWe developed a methodology able to explore and target topics of interest via an interactive user interface for health professionals with limited computer science knowledge. We aim to reach near state-of-the-art performance while reducing memory consumption, increasing scalability, and minimizing user interaction effort to improve the clinical decision-making process. The performance was evaluated on diabetes-related abstracts from PubMed.MethodsThe methodology consists of 4 parts: (1) a novel interpretable hierarchical clustering of documents where each node is defined by headwords (words that best represent the documents in the node), (2) an efficient classification system to target topics, (3) minimized user interaction effort through active learning, and (4) a visual user interface. We evaluated our approach on 50,911 diabetes-related abstracts providing a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against 3 other strategies: random selection of training instances, uncertainty sampling that chooses instances about which the model is most uncertain, and an expected gradient length strategy based on convolutional neural networks (CNNs).ResultsFor the hierarchical clustering performance, we achieved an F1 score of 0.73 compared to 0.76 achieved by scikit-learn. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1 score of all MeSH codes resulted in a satisfying 0.62 F1 score using our approach, 0.61 using the uncertainty strategy, 0.63 using the CNN, and 0.45 using the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents.ConclusionsWe proposed an easy-to-use tool for health professionals with limited computer science knowledge who combine their domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore, our approach is memory efficient and highly parallelizable, making it interesting for large Big Data sets. This approach can be used by health professionals to gain deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.

Dataset Information

Development of a transparent interactive decision interrogator to facilitate the decision-making process in health care.

Background

Objectives

Methods

Conclusion

Publications

Development of a transparent interactive decision interrogator to facilitate the decision-making process in health care.

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