Building Data-Driven Pathways From Routinely Collected Hospital Data:A Case Study on Prostate Cancer

Background: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. Conclusions: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals

Methods, systems, and devices for treating tinnitus with VNS pairing

A method of treating tinnitus comprising measuring a patient's hearing, determining the patient's hearing loss and the patient's tinnitus frequency using the measurements of the patient's hearing, programming a clinical controller with the measurements of the patient's hearing, selecting a plurality of therapeutic tones, where the therapeutic tones are selected to be at least a half-octave above or below of the patient's tinnitus frequency, setting an appropriate volume for each of the plurality of tones, repetitively playing each of the plurality of therapeutic tones, and pairing a vagus nerve stimulation pulse train with each playing of a therapeutic tone, thereby reducing the patient's perception of tinnitus.Board of Regents, University of Texas Syste

A Multi-level Approach for Identifying Process Change in Cancer Pathways

An understudied challenge within process mining is the area of process change over time. This is a particular concern in healthcare, where patterns of care emerge and evolve in response to individual patient needs and through complex interactions between people, process, technology and changing organisational structure. We propose a structured approach to analyse process change over time suitable for the complex domain of healthcare. Our approach applies a qualitative process comparison at three levels of abstraction: a holistic perspective summariz-ing patient pathways (process model level), a middle level perspective based on activity sequences for individuals (trace level), and a fine-grained detail focus on activities (activity level). Our aim is to identify points in time where a process changed (detection), to localise and characterise the change (localisation and characterisation), and to understand process evolution (unravelling). We illus-trate the approach using a case study of cancer pathways in Leeds Cancer Centre where we found evidence of agreement in process change identified at the pro-cess model and activity levels, but not at the trace level. In the experiment we show that this qualitative approach provides a useful understanding of process change over time. Examining change at the three levels provides confirmatory ev-idence of process change where perspectives agree, while contradictory evidence can lead to focused discussions with domain experts. The approach should be of interest to others dealing with processes that undergo complex change over time

Incorporating comorbidities into latent treatment pattern mining for clinical pathways

AbstractIn healthcare organizational settings, the design of a clinical pathway (CP) is challenging since patients following a particular pathway may have not only one single first-diagnosis but also several typical comorbidities, and thus it requires different disciplines involved to put together their partial knowledge about the overall pathway. Although many data mining techniques have been proposed to discover latent treatment information for CP analysis and reconstruction from a large volume of clinical data, they are specific to extract nontrivial information about the therapy and treatment of the first-diagnosis. The influence of comorbidities on adopting essential treatments is crucial for a pathway but has seldom been explored. This study proposes to extract latent treatment patterns that characterize essential treatments for both first-diagnosis and typical comorbidities from the execution data of a pathway. In particular, we propose a generative statistical model to extract underlying treatment patterns, unveil the latent associations between diagnosis labels (including both first-diagnosis and comorbidities) and treatments, and compute the contribution of comorbidities in these patterns. The proposed model extends latent Dirichlet allocation with an additional layer for diagnosis modeling. It first generates a set of latent treatment patterns from diagnosis labels, followed by sampling treatments from each pattern. We verify the effectiveness of the proposed model on a real clinical dataset containing 12,120 patient traces, which pertain to the unstable angina CP. Three treatment patterns are discovered from data, indicating latent correlations between comorbidities and treatments in the pathway. In addition, a possible medical application in terms of treatment recommendation is provided to illustrate the potential of the proposed model. Experimental results indicate that our approach can discover not only meaningful latent treatment patterns exhibiting comorbidity focus, but also implicit changes of treatments of first-diagnosis due to the incorporation of typical comorbidities potentially

PathwAI Systems in Healthcare – a Framework for Coupling AI and Pathway-based Health Information Systems

Pathway-based Health Information Systems (HIS) enable planning, execution and improvement of standardized care processes. Adaptive behavior and learning effects are taken to a new level by advances in Artificial Intelligence (AI). Yet, design support to unlock synergies from coupling pathway-based HIS with AI is lacking. This Umbrella Review identifies applied purposes of AI in healthcare, describes the relation to pathway-based HIS, and derives a PathwAI Framework as design support for future research and development activities. Previous findings already provide a large base of approaches to realize personalized care pathways and improve coordination and business operations. Furthermore, potentials for designing learning health systems at micro, meso, and macro levels are formulated, but there is still greater opportunity for future research and design. Pathway-based HIS in this context can not only provide interpretable and interoperable data input, but can be conceptual as well as operational receivers of artificially generated knowledge

People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information