Use of Real-World FHIR Data Combined with Context-Sensitive Decision Modeling to Guide Sentinel Biopsy in Melanoma.

Background: To support clinical decision-making at the point of care, the "best next step" based on Standard Operating Procedures (SOPs) and actual accurate patient data must be provided. To do this, textual SOPs have to be transformed into operable clinical algorithms and linked to the data of the patient being treated. For this linkage, we need to know exactly which data are needed by clinicians at a certain decision point and whether these data are available. These data might be identical to the data used within the SOP or might integrate a broader view. To address these concerns, we examined if the data used by the SOP is also complete from the point of view of physicians for contextual decision-making. Methods: We selected a cohort of 67 patients with stage III melanoma who had undergone adjuvant treatment and mainly had an indication for a sentinel biopsy. First, we performed a step-by-step simulation of the patient treatment along our clinical algorithm, which is based on a hospital-specific SOP, to validate the algorithm with the given Fast Healthcare Interoperability Resources (FHIR)-based data of our cohort. Second, we presented three different decision situations within our algorithm to 10 dermatooncologists, focusing on the concrete patient data used at this decision point. The results were conducted, analyzed, and compared with those of the pure algorithmic simulation. Results: The treatment paths of patients with melanoma could be retrospectively simulated along the clinical algorithm using data from the patients' electronic health records. The subsequent evaluation by dermatooncologists showed that the data used at the three decision points had a completeness between 84.6% and 100.0% compared with the data used by the SOP. At one decision point, data on "patient age (at primary diagnosis)" and "date of first diagnosis" were missing. Conclusions: The data needed for our decision points are available in the FHIR-based dataset. Furthermore, the data used at decision points by the SOP and hence the clinical algorithm are nearly complete compared with the data required by physicians in clinical practice. This is an important precondition for further research focusing on presenting decision points within a treatment process integrated with the patient data needed.

Guideline-Based Context-Sensitive Decision Modeling for Melanoma Patients.

INTRODUCTION: The provision of knowledge through clinical practice guidelines and hospital-specific standard operating procedures (SOPs) is ubiquitous in the medical context and in the treatment of melanoma patients. However, these knowledge sources are only available in unstructured text form and without any contextual link to real patient data. The aim of our project is to give a modeled decision support for the next treatment step based on the actual data and position of a patient. METHODS: First, we identified passages for qualified decision-making necessary at the point of care from the SOP for melanoma. Thereby, the patient-specific contextual reference data at decision points was considered in parallel and represented by FHIR (Fast Healthcare Interoperability Resource) resources. The decision algorithm was then formalized using BPMN modeling with FHIR annotations. Validation was provided by medical experts, dermatooncologists from University Hospital Essen. RESULTS: The resulting BPMN model is presented here with the diagnostic procedure of sentinel lymph node excision as the example snippet from the whole algorithm. Each decision point is edited with FHIR resources covering the patient data and preparing the context sensitivity of the model. CONCLUSION: Modeling guideline-based information into a decision algorithm that can be presented at the point of care with contextual reference, may have the potential to support patient-specific clinical decision-making. For patients from a certain status like in the metastatic setting modeling becomes highly tailored to specific patient cases, alternative and individualized treatment options.

Mapping Patient Data to Colorectal Cancer Clinical Algorithms for Personalized Guideline-Based Treatment.

BACKGROUND: Colorectal cancer is the most commonly occurring cancer in Germany, and the second and third most commonly diagnosed cancer in women and men, respectively. In this context, evidence-based guidelines positively impact the quality of treatment processes for cancer patients. However, evidence of their impact on real-world patient care remains unclear. To ensure the success of clinical guidelines, a fast and clear provision of knowledge at the point of care is essential. OBJECTIVES: The objectives of this study are to model machine-readable clinical algorithms for colon carcinoma and rectal carcinoma annotated by Unified Medical Language System (UMLS) based on clinical guidelines and the development of an open-source workflow system for mapping clinical algorithms with patient-specific information to identify patient's position on the treatment algorithm for guideline-based therapy recommendations. METHODS: This study qualitatively assesses the therapy decision of clinical algorithms as part of a clinical pathway. The solution uses rule-based clinical algorithms, which were developed based on the corresponding guidelines. These algorithms are executed on a newly developed open-source workflow system and are visualized at the point of care. The aim of this approach is to create clinical algorithms based on an established business process standard, the Business Process Model and Notation (BPMN), which is annotated by UMLS terminologies. The gold standard for the validation process was set by manual extraction of clinical datasets from 86 rectal cancer patients and 89 colon cancer patients. RESULTS: Using this approach, the algorithm achieved a precision value of 87.64% for colon cancer and 84.70% for rectal cancer with recall values of 87.64 and 83.72%, respectively. CONCLUSION: The results indicate that the automatic positioning of a patient on the decision pathway is possible with tumor stages that have a less complex clinical algorithm with fewer decision points reaching a higher accuracy than complex stages.

Natural language processing of German clinical colorectal cancer notes for guideline-based treatment evaluation.

BACKGROUND: Colorectal cancer is the most commonly occurring cancer in Germany, and the second and third most commonly diagnosed cancer in women and men, respectively. The therapy for this disease is based primarily on the tumor stages, which are usually documented in an unstructured form in medical information systems. In order to re-use this knowledge, the information must be extracted and annotated using the correct terminology. METHODS: In this study, a natural language processing pipeline is developed to identify specific guideline-based patient information and to annotate it with Unified Medical Language System concepts for manual evaluation by a physician. The gold standard for one-time evaluation is determined using the human abstraction of 2513 German clinical notes from electronic health records. RESULTS: Using this approach to process the narrative clinical notes on colorectal cancer for retrospective evaluation of the therapy recommendation, the algorithm achieves a precision value of 96.64% for tumor stage detection and 97.95% for diagnosis recognition with recall values of 94.89% and 99.54%, respectively. The average precision value across all concepts relevant to treatment decisions for patients with known cancer diagnoses (11 concept groups) achieved a precision value of 82.05% with a recall value of 82.45% and an F1-score of 81.81%, respectively. CONCLUSIONS: The identification of guideline-based information from narrative clinical notes has the potential for implementation as clinical decision support tools.

Using MEESTAR to Identify Ethical and Social Issues Implementing a Digital Patient-Centered Care Platform.

The PIQ research project ("Pflege im Quartier") aims for optimizing communication and patient-centered care for elderly people by implementing a digital, patient-centered care platform, with particular attention is paid to the consideration of ethical, legal and social issues. In this work, an instrument for the ethical evaluation of social-technical arrangement was used to map features of the platform with ethical concerns. The results include precise ethical questions based on scenarios identified and possible solutions to address them, regarding the ethical and privacy issues. These insights will be continuously integrated in the future system design and implementation as well as research.

Personalized Guideline-Based Treatment Recommendations Using Natural Language Processing Techniques.

Clinical guidelines and clinical pathways are accepted and proven instruments for quality assurance and process optimization. Today, electronic representation of clinical guidelines exists as unstructured text, but is not well-integrated with patient-specific information from electronic health records. Consequently, generic content of the clinical guidelines is accessible, but it is not possible to visualize the position of the patient on the clinical pathway, decision support cannot be provided by personalized guidelines for the next treatment step. The Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) provides common reference terminology as well as the semantic link for combining the pathways and the patient-specific information. This paper proposes a model-based approach to support the development of guideline-compliant pathways combined with patient-specific structured and unstructured information using SNOMED CT. To identify SNOMED CT concepts, a software was developed to extract SNOMED CT codes out of structured and unstructured German data to map these with clinical pathways annotated in accordance with the systematized nomenclature.

Semi-Automatic Mark-Up and UMLS Annotation of Clinical Guidelines.

Clinical guidelines and clinical pathways are accepted and proven instruments for quality assurance and process optimization in the healthcare domain. To derive clinical pathways from clinical guidelines, the imprecise, non-formalized abstract guidelines must be formalized. The transfer of evidence-based knowledge (clinical guidelines) to care processes (clinical pathways) is not straightforward due to different information contents and semantical constructs. A complex step within this formalization process is the mark-up step and annotation of the text passages to terminologies. The Unified Medical Language System (UMLS) provides a common reference terminology as well as the semantic link for combining the clinical pathways to patient-specific information. This paper proposes a semi-automated mark-up and UMLS annotation for clinical guidelines by using natural language processing techniques. The algorithm has been tested and evaluated using a German breast cancer guideline.

An Interdisciplinary Approach Between Medical Informatics and Social Sciences to Transdisciplinary Requirements Engineering for an Integrated Care Setting.

Requirements engineering of software products for elderly people faces some special challenges to ensure a maximum of user acceptance. Within the scope of a research project, a web-based platform and a mobile app are approached to enable people to live in their own home as long as possible. This paper is about a developed method of interdisciplinary requirements engineering by a team of social scientists in cooperation with computer scientists.

Extraction of UMLS® Concepts Using Apache cTAKES™ for German Language.

Automatic information extraction of medical concepts and classification with semantic standards from medical reports is useful for standardization and for clinical research. This paper presents an approach for an UMLS concept extraction with a customized natural language processing pipeline for German clinical notes using Apache cTAKES. The objectives are, to test the natural language processing tool for German language if it is suitable to identify UMLS concepts and map these with SNOMED-CT. The German UMLS database and German OpenNLP models extended the natural language processing pipeline, so the pipeline can normalize to domain ontologies such as SNOMED-CT using the German concepts. For testing, the ShARe/CLEF eHealth 2013 training dataset translated into German was used. The implemented algorithms are tested with a set of 199 German reports, obtaining a result of average 0.36 F1 measure without German stemming, pre- and post-processing of the reports.

Virtual Oncological Networks--IT Support for an Evidence-based, Oncological Health Care Management.

An interdisciplinary and intersectoral coordinated therapy management along Clinical Practice Guidelines can ensure that all patients receive adequate diagnostic, treatment, and supportive services that lead most likely to optimal outcomes. Within the research project "Virtual Oncological Networks", guideline-compliant pathways are defined and enacted within a Health Care Management Platform to support treatment planning and ongoing care of oncological diseases.

Managing integrated oncology treatment in virtual networks.

Interdisciplinary and intersectoral coordinated healthcare management based on Clinical Practice Guidelines is essential to achieve high quality in oncological networks. The objective of our research project is to create a cookbook, which can be used by oncological networks as a template. The cookbook is based on guideline-compliant care processes. To develop these care processes, the three S3-guidelines breast, colon and prostate carcinoma have been formalized. The thus-obtained platform-independent process fragments were transformed into an underlying metamodel, which is based on HL7 and can be used for modeling clinical pathways. Additional, qualitative guided interviews were chosen to capitalize on the experts' (e.g. chief residents, resident specialists) wide knowledge and experience in oncological health care management. One of these use cases (tumor board scheduling) is developed for a healthcare management platform which is linked to a national electronic case record. The projected result of our approach is a cookbook which shows, how the treatment can be controlled by interdisciplinary and intersectoral care processes in an oncological network.

Towards IHE profiles for e-supply in the healthcare domain.

Procurement and supply belong to the crucial supporting processes in hospitals. By providing the right products at the right time in the right quantity in the right place, these processes are of major importance for patient safety and the success of the primary hospital process cure and care. Though of such an importance, the hospital's internal and external procurement and supply processes are still not performed seamlessly by information systems, due to a variety of electronic standards for procurement and supply. These different standards and missing guidelines for electronic procurement and supply in the healthcare domain lead to little or no interoperability between the participating procurement and supply systems. Consequences can be delayed or wrong supply, and increased costs. To overcome this deficiency this work describes the derivation of a common guideline for the implementation of electronically enabled supply processes in the healthcare domain based upon the identification of commonly used electronic standards and detailed specifications of procurement processes in hospital facilities.

PathGuide - model-based generation of guideline-compliant pathways for the use in different hospital information systems.

Clinical guidelines and clinical pathways are accepted instruments for quality assurance and process optimization. Defining a standardized patient treatment for a specific disease clinical guidelines provide evident medical knowledge for diagnostic and therapeutic issues in form of narrative recommendations. Clinical pathways offer process-like descriptions defining daily diagnostic and treatment procedures. The use of evident knowledge (in form of clinical guidelines) during pathway development is highly recommended. But the transfer of unstructured recommendations to care processes (clinical pathways) is not straightforward. This article proposes a model-based approach to support the development of guideline-compliant pathways and focuses their deployment into different target systems. Especially HIS (hospital information system) are considered as this is the critical system for managing patient care processes in the hospital. So the results of latest research provided by clinical guidelines are implemented into daily practice and therefore improve patient care.

Structured knowledge acquisition for defining guideline-compliant pathways.

Healthcare providers are facing an enormous cost pressure, wherefore the assurance of an efficient care on a high level of quality is of decisive importance. Clinical guidelines and clinical pathways have been established for that purpose. Clinical guidelines offer abstract recommendations for diagnostic and therapeutic issues, while clinical pathways are a road map of patient management. The consideration of clinical guidelines during pathway development is highly recommended. But the transfer of evident knowledge (clinical guidelines) to care processes (clinical pathways) is not straightforward due to different information contents and semantical constructs. This article proposes a model-driven approach in conjunction with a developed knowledge acquisition tool to improve the development of guideline-compliant pathways.

Extracting and transforming clinical guidelines into pathway models for different hospital information systems.

BACKGROUND: Healthcare providers are facing an enormous cost pressure and a scarcity of resources. They need to realign in the tension between economic efficiency and demand-oriented healthcare. Clinical guidelines and clinical pathways are used in German hospitals to improve the quality of care and to reduce costs at the same time. Clinical guidelines provide evident medical knowledge for diagnostic and therapeutic issues, while clinical pathways are a road map of patient management. The consideration of clinical guidelines during pathway development is highly recommended. But the transfer of evident knowledge (clinical guidelines) to care processes (clinical pathways) is not straightforward due to different information contents and structures. METHODS: We propose a model-based approach to support the development of guideline-compliant pathways and the generation of ready-to-use pathway models for different hospital information systems. A meta-model merges the structures of clinical guidelines and clinical pathways into one generic model. It is encoded through artefacts of Health Level 7 (HL7) in version 3. The deployment process to integrate the defined guideline-compliant pathways into different target systems is supported by an ontology management approach. RESULTS: We defined a step-by-step instruction for translating the narrative guideline content into formalized care processes. The meta-model provides all necessary structures to capture the pertinent knowledge. The entire process of defining and deploying guideline-compliant pathways is supported by one consistent IT system. The deployment process is designed detached from specific systems so that the defined pathways can be enacted within different hospital information systems (HIS). CONCLUSIONS: The approach enables hospitals to develop guideline-compliant pathways and to integrate them into their HIS without time-consuming manual transformations. That way, best practice advices based on clinical guidelines can be provided at the point of care and therefore improve patient treatment.