Using data mining techniques to explore physicians' therapeutic decisions when clinical guidelines do not provide recommendations: methods and example for type 2 diabetes

<p>Abstract</p> <p>Background</p> <p>Clinical guidelines carry medical evidence to the point of practice. As evidence is not always available, many guidelines do not provide recommendations for all clinical situations encountered in practice. We propose an approach for identifying knowledge gaps in guidelines and for exploring physicians' therapeutic decisions with data mining techniques to fill these knowledge gaps. We demonstrate our method by an example in the domain of type 2 diabetes.</p> <p>Methods</p> <p>We analyzed the French national guidelines for the management of type 2 diabetes to identify clinical conditions that are not covered or those for which the guidelines do not provide recommendations. We extracted patient records corresponding to each clinical condition from a database of type 2 diabetic patients treated at Avicenne University Hospital of Bobigny, France. We explored physicians' prescriptions for each of these profiles using C5.0 decision-tree learning algorithm. We developed decision-trees for different levels of detail of the therapeutic decision, namely the type of treatment, the pharmaco-therapeutic class, the international non proprietary name, and the dose of each medication. We compared the rules generated with those added to the guidelines in a newer version, to examine their similarity.</p> <p>Results</p> <p>We extracted 27 rules from the analysis of a database of 463 patient records. Eleven rules were about the choice of the type of treatment and thirteen rules about the choice of the pharmaco-therapeutic class of each drug. For the choice of the international non proprietary name and the dose, we could extract only a few rules because the number of patient records was too low for these factors. The extracted rules showed similarities with those added to the newer version of the guidelines.</p> <p>Conclusion</p> <p>Our method showed its usefulness for completing guidelines recommendations with rules learnt automatically from physicians' prescriptions. It could be used during the development of guidelines as a complementary source from practice-based knowledge. It can also be used as an evaluation tool for comparing a physician's therapeutic decisions with those recommended by a given set of clinical guidelines. The example we described showed that physician practice was in some ways ahead of the guideline.</p

A novel method for measuring patients' adherence to insulin dosing guidelines: introducing indicators of adherence

<p>Abstract</p> <p>Background</p> <p>Diabetic type 1 patients are often advised to use dose adjustment guidelines to calculate their doses of insulin. Conventional methods of measuring patients' adherence are not applicable to these cases, because insulin doses are not determined in advance. We propose a method and a number of indicators to measure patients' conformance to these insulin dosing guidelines.</p> <p>Methods</p> <p>We used a database of logbooks of type 1 diabetic patients who participated in a summer camp. Patients used a guideline to calculate the doses of insulin lispro and glargine four times a day, and registered their injected doses in the database. We implemented the guideline in a computer system to calculate recommended doses. We then compared injected and recommended doses by using five indicators that we designed for this purpose: absolute agreement (AA): the two doses are the same; relative agreement (RA): there is a slight difference between them; extreme disagreement (ED): the administered and recommended doses are merely opposite; Under-treatment (UT) and over-treatment (OT): the injected dose is not enough or too high, respectively. We used weighted linear regression model to study the evolution of these indicators over time.</p> <p>Results</p> <p>We analyzed 1656 insulin doses injected by 28 patients during a three weeks camp. Overall indicator rates were AA = 45%, RA = 30%, ED = 2%, UT = 26% and OT = 30%. The highest rate of absolute agreement is obtained for insulin glargine (AA = 70%). One patient with alarming behavior (AA = 29%, RA = 24% and ED = 8%) was detected. The monitoring of these indicators over time revealed a crescendo curve of adherence rate which fitted well in a weighted linear model (slope = 0.85, significance = 0.002). This shows an improvement in the quality of therapeutic decision-making of patients during the camp.</p> <p>Conclusion</p> <p>Our method allowed the measurement of patients' adherence to their insulin adjustment guidelines. The indicators that we introduced were capable of providing quantitative data on the quality of patients' decision-making for the studied population as a whole, for each individual patient, for all injections, and for each time of injection separately. They can be implemented in monitoring systems to detect non-adherent patients.</p

Uncertainty management in regulatory and health technology assessment decision-making on drugs: guidance of the HTAi-DIA Working Group

ObjectivesUncertainty is a fundamental component of decision making regarding access to and pricing and reimbursement of drugs. The context-specific interpretation and mitigation of uncertainty remain major challenges for decision makers. Following the 2021 HTAi Global Policy Forum, a cross-sectoral, interdisciplinary HTAi-DIA Working Group (WG) was initiated to develop guidance to support stakeholder deliberation on the systematic identification and mitigation of uncertainties in the regulatory-HTA interface. MethodsSix online discussions among WG members (Dec 2021-Sep 2022) who examined the output of a scoping review, two literature-based case studies and a survey; application of the initial guidance to a real-world case study; and two international conference panel discussions. ResultsThe WG identified key concepts, clustered into twelve building blocks that were collectively perceived to define uncertainty: "unavailable," "inaccurate," "conflicting," "not understandable," "random variation," "information," "prediction," "impact," "risk," "relevance," "context," and "judgment." These were converted into a checklist to explain and define whether any issue constitutes a decision-relevant uncertainty. A taxonomy of domains in which uncertainty may exist within the regulatory-HTA interface was developed to facilitate categorization. The real-world case study was used to demonstrate how the guidance may facilitate deliberation between stakeholders and where additional guidance development may be needed. ConclusionsThe systematic approach taken for the identification of uncertainties in this guidance has the potential to facilitate understanding of uncertainty and its management across different stakeholders involved in drug development and evaluation. This can improve consistency and transparency throughout decision processes. To further support uncertainty management, linkage to suitable mitigation strategies is necessary

HARmonized Protocol Template to Enhance Reproducibility of hypothesis evaluating real-world evidence studies on treatment effects: A good practices report of a joint ISPE/ISPOR task force

PROBLEM: Ambiguity in communication of key study parameters limits the utility of real-world evidence (RWE) studies in healthcare decision-making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias. WHAT WE DID: The International Society for Pharmacoepidemiology (ISPE) and ISPOR-The Professional Society for Health Economics and Outcomes Research (ISPOR) convened a joint task force, including representation from key international stakeholders, to create a harmonized protocol template for RWE studies that evaluate a treatment effect and are intended to inform decision-making. The template builds on existing efforts to improve transparency and incorporates recent insights regarding the level of detail needed to enable RWE study reproducibility. The overarching principle was to reach for sufficient clarity regarding data, design, analysis, and implementation to achieve 3 main goals. One, to help investigators thoroughly consider, then document their choices and rationale for key study parameters that define the causal question (e.g., target estimand), two, to facilitate decision-making by enabling reviewers to readily assess potential for biases related to these choices, and three, to facilitate reproducibility. STRATEGIES TO DISSEMINATE AND FACILITATE USE: Recognizing that the impact of this harmonized template relies on uptake, we have outlined a plan to introduce and pilot the template with key international stakeholders over the next 2 years. CONCLUSION: The HARmonized Protocol Template to Enhance Reproducibility (HARPER) helps to create a shared understanding of intended scientific decisions through a common text, tabular and visual structure. The template provides a set of core recommendations for clear and reproducible RWE study protocols and is intended to be used as a backbone throughout the research process from developing a valid study protocol, to registration, through implementation and reporting on those implementation decisions

HARmonized Protocol Template to Enhance Reproducibility of Hypothesis Evaluating Real-World Evidence Studies on Treatment Effects: A Good Practices Report of a Joint ISPE/ISPOR Task Force

Objectives: Ambiguity in communication of key study parameters limits the utility of real-world evidence (RWE) studies in healthcare decision-making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias. Methods: The International Society for Pharmacoepidemiology (ISPE) and ISPOR–The Professional Society for Health Economics and Outcomes Research (ISPOR) convened a joint task force, including representation from key international stakeholders, to create a harmonized protocol template for RWE studies that evaluate a treatment effect and are intended to inform decision-making. The template builds on existing efforts to improve transparency and incorporates recent insights regarding the level of detail needed to enable RWE study reproducibility. The over-arching principle was to reach for sufficient clarity regarding data, design, analysis, and implementation to achieve 3 main goals. One, to help investigators thoroughly consider, then document their choices and rationale for key study parameters that define the causal question (e.g., target estimand), two, to facilitate decision-making by enabling reviewers to readily assess potential for biases related to these choices, and three, to facilitate reproducibility. Strategies to Disseminate and Facilitate Use: Recognizing that the impact of this harmonized template relies on uptake, we have outlined a plan to introduce and pilot the template with key international stakeholders over the next 2 years. Conclusion: The HARmonized Protocol Template to Enhance Reproducibility (HARPER) helps to create a shared understanding of intended scientific decisions through a common text, tabular and visual structure. The template provides a set of core recommendations for clear and reproducible RWE study protocols and is intended to be used as a backbone throughout the research process from developing a valid study protocol, to registration, through implementation and reporting on those implementation decisions

HARmonized Protocol Template to Enhance Reproducibility of hypothesis evaluating real-world evidence studies on treatment effects: A good practices report of a joint ISPE/ISPOR task force.

PROBLEM: Ambiguity in communication of key study parameters limits the utility of real-world evidence (RWE) studies in healthcare decision-making. Clear communication about data provenance, design, analysis, and implementation is needed. This would facilitate reproducibility, replication in independent data, and assessment of potential sources of bias. WHAT WE DID: The International Society for Pharmacoepidemiology (ISPE) and ISPOR-The Professional Society for Health Economics and Outcomes Research (ISPOR) convened a joint task force, including representation from key international stakeholders, to create a harmonized protocol template for RWE studies that evaluate a treatment effect and are intended to inform decision-making. The template builds on existing efforts to improve transparency and incorporates recent insights regarding the level of detail needed to enable RWE study reproducibility. The overarching principle was to reach for sufficient clarity regarding data, design, analysis, and implementation to achieve 3 main goals. One, to help investigators thoroughly consider, then document their choices and rationale for key study parameters that define the causal question (e.g., target estimand), two, to facilitate decision-making by enabling reviewers to readily assess potential for biases related to these choices, and three, to facilitate reproducibility. STRATEGIES TO DISSEMINATE AND FACILITATE USE: Recognizing that the impact of this harmonized template relies on uptake, we have outlined a plan to introduce and pilot the template with key international stakeholders over the next 2 years. CONCLUSION: The HARmonized Protocol Template to Enhance Reproducibility (HARPER) helps to create a shared understanding of intended scientific decisions through a common text, tabular and visual structure. The template provides a set of core recommendations for clear and reproducible RWE study protocols and is intended to be used as a backbone throughout the research process from developing a valid study protocol, to registration, through implementation and reporting on those implementation decisions

Analyse et reconstitution des décisions thérapeutiques des médecins et des patients à partir des données enregistrées dans les dossiers patient informatisés

Cette thèse a trait à l étude de la décision thérapeutique et de sa conformité aux recommandations contenues dans les guides de bonnes pratiques. Nous proposons trois méthodes pour l analyse et la reconstitution des décisions des médecins et des patients à partir des données enregistrées dans les dossiers patients. Notre première méthode porte sur l analyse de la conformité des prescriptions vis-à-vis des recommandations de bonnes pratiques. Cette analyse s appuie sur une typologie des traitements qui permet de formaliser les prescriptions et les recommandations et de les comparer à trois niveaux de détails: le type de traitement, la classe pharmaco-thérapeutique, et la dose. Notre deuxième méthode porte sur l extraction des décisions thérapeutiques des médecins à partir des dossiers patients quand les guides de bonnes pratiques ne proposent pas de recommandations. Nous présentons d abord une méthode de découverte des lacunes de connaissances d un guide de bonnes pratiques. Ensuite, nous appliquons un algorithme d apprentissage automatique (C5.0 de Quinlan) à une base de données des dossiers patients pour extraire de nouvelles règles que nous greffons à l arbre de décision original du guide. Notre troisième méthode porte sur l analyse de la conformité des décisions thérapeutiques des patients vis-à-vis des recommandations des médecins concernant l ajustement des doses d insuline. Nous présentons cinq indicateurs qui permettent de vérifier le niveau de l observance des patients:l accord absolu (AA) et l accord relatif (RA) montrent une observance acceptable, le désaccord extrême (ED) montre un comportement dangereux, le sur-traitement (OT) et le sous-traitement (UT) montrent respectivement l administration d une dose trop forte ou trop faible de médicament.This thesis deals with the study of the agreement between the therapeutic decisions and the recommendations of best practice. We propose three methods for the analysis and the reconstruction of physicians and patients therapeutic decisions through the information available in patient records. Our first method involves the analysis of the agreement between physicians prescriptions and the recommendations of best practice. We present a typology of drug therapy, applicable to chronic disease, allowing to formalize both prescriptions and recommendations and to compare them in three levels of detail: the type of treatment, pharmaco-therapeutic class, and the dose of each medication. Our second method involves the extraction of physicians therapeutic decisions through patient records when the guidelines do not offer recommendations. We first present a method for discovering knowledge gaps in clinical practice guidelines. Then we apply a machine learning algorithm (C5.0 Quinlan) to a database of patient records to extract new rules that we graft to the decision tree of the original guideline. Our third method involves the analysis of compliance of patients therapeutic decisions with regard to the physicians recommendations concerning insulin dose adjustment. We present five indicators useful for the verification of the level of patient compliance: absolute agreement (AA) and the relative agreement (RA) show an acceptable compliance, extreme disagreement (ED) shows a dangerous behavior, over-treatment (OT) and under-treatment (UT) show that the administered dose was respectively too high or too low.PARIS13-BU Sciences (930792102) / SudocSudocFranceF

Representing the Patient's Therapeutic History in Medical Records and in Guideline Recommendations for Chronic Diseases Using a Unique Model.

International audienceComputer-interpretable guidelines (CIGs) are more likely to affect the clinician's behavior when they deliver patient-specific and just-in-time clinical advice. CIGs must take into account the data stored in the patient's electronic medical records (EMR). For chronic diseases, the outcome of past and ongoing treatments (therapeutic history) is used in the clinical guidelines. We propose a model for the conceptualization of therapeutic history, facilitating data sharing between EMRs and CIGs and the representation of therapeutic history and recommended treatments in clinical guidelines.Based on medical literature review and an existing treatment model, a core structure is first defined taking into account drug and non-drug treatment components and treatment type (e.g. bitherapy). These elements together with additional concepts obtained by analyzing a sample guideline relating to diabetes, are then organized into an object-oriented model, using UML formalism.We show how this model can be used to store the patient's therapeutic history in the EMR, together with other attributes such as treatment efficacy and tolerance. We also explain how this model can efficiently code guidelines therapeutic rules.We evaluated this model, using additional guidelines hypercholesterolemia and asthma. We found it capable for representing guideline recommendations in several domains of chronic diseases

Representing the Patient's Therapeutic History in Medical Records and in Guideline Recommendations for Chronic Diseases Using a Unique Model.

International audienceComputer-interpretable guidelines (CIGs) are more likely to affect the clinician's behavior when they deliver patient-specific and just-in-time clinical advice. CIGs must take into account the data stored in the patient's electronic medical records (EMR). For chronic diseases, the outcome of past and ongoing treatments (therapeutic history) is used in the clinical guidelines. We propose a model for the conceptualization of therapeutic history, facilitating data sharing between EMRs and CIGs and the representation of therapeutic history and recommended treatments in clinical guidelines.Based on medical literature review and an existing treatment model, a core structure is first defined taking into account drug and non-drug treatment components and treatment type (e.g. bitherapy). These elements together with additional concepts obtained by analyzing a sample guideline relating to diabetes, are then organized into an object-oriented model, using UML formalism.We show how this model can be used to store the patient's therapeutic history in the EMR, together with other attributes such as treatment efficacy and tolerance. We also explain how this model can efficiently code guidelines therapeutic rules.We evaluated this model, using additional guidelines hypercholesterolemia and asthma. We found it capable for representing guideline recommendations in several domains of chronic diseases

Microvascular Outcomes in Patients with Type 2 Diabetes Treated with Vildagliptin vs. Sulfonylurea: A Retrospective Study Using German Electronic Medical Records

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