Enea Parimbelli

Data Quality and Completeness in a Web Stroke Registry as the Basis for Data and Process Mining

Electronic health records often show missing values and errors jeopardizing their effective exploitation. We illustrate the re-engineering process needed to improve the data quality of a web-based, multicentric stroke registry by proposing a knowledge-based data entry support able to help users to homogeneously interpret data items, and to prevent and detect treacherous errors. The re-engineering also improves stroke units coordination and networking, through ancillary tools for monitoring patient enrollments, calculating stroke care indicators, analyzing compliance with clinical practice guidelines, and entering stroke units profiles. Finally we report on some statistics, such as calculation of indicators for assessing the quality of stroke care, data mining for knowledge discovery, and process mining for comparing different processes of care delivery. The most important results of the re-engineering are an improved user experience with data entry, and a definitely better data quality that guarantees the reliability of data analyses.

MobiGuide: a personalized and patient-centric decision-support system and its evaluation in the atrial fibrillation and gestational diabetes domains

MobiGuide is a ubiquitous, distributed and personalized evidence-based decision-support system (DSS) used by patients and their care providers. Its central DSS applies computer-interpretable clinical guidelines (CIGs) to provide real-time patient-specific and personalized recommendations by matching CIG knowledge with a highly-adaptive patient model, the parameters of which are stored in a personal health record (PHR). The PHR integrates data from hospital medical records, mobile biosensors, data entered by patients, and recommendations and abstractions output by the DSS. CIGs are customized to consider the patients’ psycho-social context and their preferences; shared decision making is supported via decision trees instantiated with patient utilities. The central DSS “projects” personalized CIG-knowledge to a mobile DSS operating on the patients’ smart phones that applies that knowledge locally. In this paper we explain the knowledge elicitation and specification methodologies that we have developed for making CIGs patient-centered and enabling their personalization. We then demonstrate feasibility, in two very different clinical domains, and two different geographic sites, as part of a multi-national feasibility study, of the full architecture that we have designed and implemented. We analyze usage patterns and opinions collected via questionnaires of the 10 atrial fibrillation (AF) and 20 gestational diabetes mellitus (GDM) patients and their care providers. The analysis is guided by three hypotheses concerning the effect of the personal patient model on patients and clinicians’ behavior and on patients’ satisfaction. The results demonstrate the sustainable usage of the system by patients and their care providers and patients’ satisfaction, which stems mostly from their increased sense of safety. The system has affected the behavior of clinicians, which have inspected the patients’ models between scheduled visits, resulting in change of diagnosis for two of the ten AF patients and anticipated change in therapy for eleven of the twenty GDM patients.

Graphical Representation of Life Paths to Better Convey Results of Decision Models to Patients

The inclusion of patients’ perspectives in clinical practice has become an important matter for health professionals, in view of the increasing attention to patient-centered care. In this regard, this report illustrates a method for developing a visual aid that supports the physician in the process of informing patients about a critical decisional problem. In particular, we focused on interpretation of the results of decision trees embedding Markov models implemented with the commercial tool TreeAge Pro. Starting from patient-level simulations and exploiting some advanced functionalities of TreeAge Pro, we combined results to produce a novel graphical output that represents the distributions of outcomes over the lifetime for the different decision options, thus becoming a more informative decision support in a context of shared decision making. The training example used to illustrate the method is a decision tree for thromboembolism risk prevention in patients with nonvalvular atrial fibrillation.

Use of the virtual medical record data model for communication among components of a distributed decision-support system

MobiGuide is a distributed decision-support system (DSS) that provides decision support for patients and physicians. Patients receive support using a light-weight Smartphone DSS linked to data arriving from wearable monitoring devices and physicians receive support via a web interface connected to a backend DSS that has access to an integrated personal health record (PHR) that stores hospital EMR data, monitoring data, and recommendations provided for the patient by the DSSs. The patient data model used by the PHR and by all the system components that interact in a service-oriented architecture is based on HL7s virtual medical record (vMR) model. We describe how we used and extended the vMR model to support communication between the system components for the complex workflow needed to support guidance of patients any time everywhere.

Interplay between Clinical Guidelines and Organizational Workflow Systems

BACKGROUND Implementing a decision-support system within a healthcare organization requires integration of clinical domain knowledge with resource constraints. Computer-interpretable guidelines (CIG) are excellent instruments for addressing clinical aspects while business process management (BPM) languages and Workflow (Wf) engines manage the logistic organizational constraints. OBJECTIVES Our objective is the orchestration of all the relevant factors needed for a successful execution of patients care pathways, especially when spanning the continuum of care, from acute to community or home care. METHODS We considered three strategies for integrating CIGs with organizational workflows: extending the CIG or BPM languages and their engines, or creating an interplay between them. We used the interplay approach to implement a set of use cases arising from a CIG implementation in the domain of Atrial Fibrillation. To provide a more scalable and standards-based solution, we explored the use of Cross-Enterprise Document Workflow Integration Profile. RESULTS We describe our proof-of-concept implementation of five use cases. We utilized the Personal Health Record of the MobiGuide project to implement a loosely-coupled approach between the Activiti BPM engine and the Picard CIG engine. Changes in the PHR were detected by polling. IHE profiles were used to develop workflow documents that orchestrate cross-enterprise execution of cardioversion. CONCLUSIONS Interplay between CIG and BPM engines can support orchestration of care flows within organizational settings.

UceWeb: a Web-based Collaborative Tool for Collecting and Sharing Quality of Life Data

OBJECTIVES This work aims at building a platform where quality-of-life data, namely utility coefficients, can be elicited not only for immediate use, but also systematically stored together with patient profiles to build a public repository to be further exploited in studies on specific target populations (e.g. cost/utility analyses). METHODS We capitalized on utility theory and previous experience to define a set of desirable features such a tool should show to facilitate sound elicitation of quality of life. A set of visualization tools and algorithms has been developed to this purpose. To make it easily accessible for potential users, the software has been designed as a web application. A pilot validation study has been performed on 20 atrial fibrillation patients. RESULTS A collaborative platform, UceWeb, has been developed and tested. It implements the standard gamble, time trade-off and rating-scale utility elicitation methods. It allows doctors and patients to choose the mode of interaction to maximize patients’ comfort in answering difficult questions. Every utility elicitation may contribute to the growth of the repository. CONCLUSION UceWeb can become a unique source of data allowing researchers both to perform more reliable comparisons among healthcare interventions and build statistical models to gain deeper insight into quality of life data.

Exploring IBM Watson to Extract Meaningful Information from the List of References of a Clinical Practice Guideline

In clinical practice, physicians often need to take decisions based both on previous experience and medical evidence. Such evidence is usually available in the form of clinical practice guidelines, which elaborate and summarize the knowledge contained in multiple documents. During clinical practice the synthetic format of medical guidelines is an advantage. However, when guidelines are used for educational purposes or when a clinician wants to gain deeper insight into a recommendation, it could be useful to examine all the guideline references relevant to a specific question. In this work we explored IBM Watson services available on the Bluemix cloud to automatically retrieve information from the wide corpus of documents referenced in a recent Italian compendium on emergency neurology. We integrated this functionality in a web application that combines multiple Watson services to index and query the referenced corpus. To evaluate the proposed approach we use the original guideline to check whether the retrieved text matches the actions mentioned in the recommendations.

A platform for targeting cost-utility analyses to specific populations

Quality-adjusted life years (QALYs) are a popular measure employed in cost-utility analysis (CUA) for informing decisions about competing healthcare programs applicable to a target population.

Combining Decision Support System-Generated Recommendations with Interactive Guideline Visualization for Better Informed Decisions

The main task of decision support systems based on computer-interpretable guidelines (CIG) is to send recommendations to physicians, combining patients’ data with guideline knowledge. Another important task is providing physicians with explanations for such recommendations. For this purpose some systems may show, for every recommendation, the guideline path activated by the reasoner. However the fact that the physician does not have a global view of the guideline may represent a limitation. Indeed, there are instances (e.g. when the clinical presentation does not perfectly fit the guideline) in which the analysis of alternatives that were not activated by the system becomes warranted. Furthermore possibly valid alternatives could not be activated due to lack of data or wrong knowledge representation. This paper illustrates a CIG implementation that complements the two functionalities, i.e., sending punctual recommendations and allowing a meaningful navigation of the entire guideline. The training example concerns atrial fibrillation management.

Collaborative Filtering for Estimating Health Related Utilities in Decision Support Systems

A distinctive feature of most advanced clinical decision support systems is the ability to adapt to habits and preferences of patients. However effective preferences elicitation is still among the most challenging tasks to achieve fully personalized guidance. On the other hand availability of data related to patients’ lives and habits is steadily increasing, making its exploitation an interesting opportunity for such purposes. In the MobiGuide project decision trees are used to implement shared-decision making using utility coefficients to incorporate patient preferences in the model. The main focus of this paper is the effort devoted to enhance traditional elicitation techniques proposing a methodology to predict patients’ health-related utility coefficients. In particular we describe a recommender system, based on collaborative filtering, capable of estimating utilities by means of integrating different data sources such as medical surveys, questionnaires and utility elicitation tools along with patient self-reported experiences in the form of natural language.