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

Conformance analysis of clinical pathway using electronic health record data

Objectives: The objective of this study was to confirm the conformance rate of the actual usage of the clinical pathway (CP) using Electronic Health Record (EHR) log data in a tertiary general university hospital to improve the CP by reflecting realworld care processes. Methods: We analyzed the application and matching rates of clinicians??? orders with predefined CP order sets based on data from 164 inpatients who received appendectomies out of all patients who were hospitalized from August 2013 to June 2014. We collected EHR log data on patient information, medication orders, operation performed, diagnosis, transfer, and CP order sets. The data were statistically analyzed. Results: The average value of the actual application rate of the prescribed CP order ranged from 0.75 to 0.89. The application rate decreased when the order date was factored in along with the order code and type. Among CP pre-operation, intra-operation, post-operation, routine, and discharge orders, orders pertaining to operations had higher application rates than other types of orders. Routine orders and discharge orders had lower application rates. Conclusions: This analysis of the application and matching rates of CP orders suggests that it is possible to improve these rates by updating the existing CP order sets for routine discharge orders to reflect data-driven evidence. This study shows that it is possible to improve the application and matching rates of the CP using EHR log data. However, further research should be performed to analyze the effects of these rates on care outcomes. © 2015 The Korean Society of Medical Informaticsopen0

Predicting Illness and Type of Treatment from Digital Health Records

Kasvavad kulud tervishoius ning samaaegne töötava populatsiooni kahanemine on kriitliine probleem kõikjal arenenud maailmas. Ühest küljest on paratamatu, et uued ravimid ja meetodid on kallid, teisest küljest on võimalik vähendada välditavaid kulutusi parema plaanimise ja ennetustööga. Enamik haiglad salvestavad digitaalselt kõik, mis patsiendiga ravi jooksul toimub ja Eestis esitatakse kõik raviarved ka Eesti Haigekassale (HK) hüvitamiseks. Käesolevas töös kasutatakse HK andmeid ehitamaks mudelit, mille abil on võimalik tuletada erinevad raviprotsessid, mida patsientide ravimisel kasutatakse ning samuti ka ennustada patsientide hulka, kes tulevikus vastavat ravi vajavad. Selline mudel võiks olla kasulik suunamaks otsuseid vahendite jaotamisel ja ennetustöö suunamisel.The rising costs of healthcare and decreasing size of the working population is a dire problem in most of the developed world. While it is inevitable that new methods are costly, it is possible to reduce avoidable expenses by better planning and prevention. Most hospitals keep digital records of everything that happens to a patient during their treatment and in Estonia all medical bills are also presented to the National Health Insurance Fund (NHIF) for reimbursement. In this work the data from NHIF is used to build a model that as the first step uncovers the different clinical pathways followed for the treatment of patients with an illness. As a second step the model is used to predict the number of patients that will be provided the uncovered treatments in the future. The output of such a model could be a valuable asset for planning resource allocation and preventative health care

Discovering medication patterns for high-complexity drug-using diseases through electronic medical records

An Electronic Medical Record (EMR) is a professional document that contains all data generated during the treatment process. The EMR can utilize various data formats, such as numerical data, text, and images. Mining the information and knowledge hidden in the huge amount of EMR data is an essential requirement for clinical decision support, such as clinical pathway formulation and evidence-based medical research. In this paper, we propose a machine-learning-based framework to mine the hidden medication patterns in EMR text. The framework systematically integrates the Jaccard similarity evaluation, spectral clustering, the modified Latent Dirichlet Allocation and cross-matching among multiple features to find the residuals that describe additional knowledge and clusters hidden in multiple perspectives of highly complex medication patterns. These methods work together, step by step to reveal the underlying medication pattern. We evaluated the method by using real data from EMR text (patients with cirrhotic ascites) from a large hospital in China. The proposed framework outperforms other approaches for medication pattern discovery, especially for this disease with subtle medication treatment variances. The results also revealed little overlap among the discovered patterns; thus, the distinct features of each pattern are well studied through the proposed framework

Can process mining automatically describe care pathways of patients with long-term conditions in UK primary care? A study protocol

Introduction In the UK, primary care is seen as the optimal context for delivering care to an ageing population with a growing number of long-term conditions. However, if it is to meet these demands effectively and efficiently, a more precise understanding of existing care processes is required to ensure their configuration is based on robust evidence. This need to understand and optimise organisational performance is not unique to healthcare, and in industries such as telecommunications or finance, a methodology known as ‘process mining’ has become an established and successful method to identify how an organisation can best deploy resources to meet the needs of its clients and customers. Here and for the first time in the UK, we will apply it to primary care settings to gain a greater understanding of how patients with two of the most common chronic conditions are managed. Methods and analysis The study will be conducted in three phases; first, we will apply process mining algorithms to the data held on the clinical management system of four practices of varying characteristics in the West Midlands to determine how each interacts with patients with hypertension or type 2 diabetes. Second, we will use traditional process mapping exercises at each practice to manually produce maps of care processes for the selected condition. Third, with the aid of staff and patients at each practice, we will compare and contrast the process models produced by process mining with the process maps produced via manual techniques, review differences and similarities between them and the relative importance of each. The first pilot study will be on hypertension and the second for patients diagnosed with type 2 diabetes

Inferring Actual Treatment Pathways from Patient Records

Treatment pathways are step-by-step plans outlining the recommended medical care for specific diseases; they get revised when different treatments are found to improve patient outcomes. Examining health records is an important part of this revision process, but inferring patients' actual treatments from health data is challenging due to complex event-coding schemes and the absence of pathway-related annotations. This study aims to infer the actual treatment steps for a particular patient group from administrative health records (AHR) - a common form of tabular healthcare data - and address several technique- and methodology-based gaps in treatment pathway-inference research. We introduce Defrag, a method for examining AHRs to infer the real-world treatment steps for a particular patient group. Defrag learns the semantic and temporal meaning of healthcare event sequences, allowing it to reliably infer treatment steps from complex healthcare data. To our knowledge, Defrag is the first pathway-inference method to utilise a neural network (NN), an approach made possible by a novel, self-supervised learning objective. We also developed a testing and validation framework for pathway inference, which we use to characterise and evaluate Defrag's pathway inference ability and compare against baselines. We demonstrate Defrag's effectiveness by identifying best-practice pathway fragments for breast cancer, lung cancer, and melanoma in public healthcare records. Additionally, we use synthetic data experiments to demonstrate the characteristics of the Defrag method, and to compare Defrag to several baselines where it significantly outperforms non-NN-based methods. Defrag significantly outperforms several existing pathway-inference methods and offers an innovative and effective approach for inferring treatment pathways from AHRs. Open-source code is provided to encourage further research in this area

On mining latent treatment patterns from electronic medical records

Clinical pathway (CP) analysis plays an important role in health-care management in ensuring specialized, standardized, normalized and sophisticated therapy procedures for individual patients. Recently, with the rapid development of hospital information systems, a large volume of electronic medical records (EMRs) has been produced, which provides a comprehensive source for CP analysis. In this paper, we are concerned with the problem of utilizing the heterogeneous EMRs to assist CP analysis and improvement. More specifically, we develop a probabilistic topic model to link patient features and treatment behaviors together to mine treatment patterns hidden in EMRs. Discovered treatment patterns, as actionable knowledge representing the best practice for most patients in most time of their treatment processes, form the backbone of CPs, and can be exploited to help physicians better understand their specialty and learn from previous experiences for CP analysis and improvement. Experimental results on a real collection of 985 EMRs collected from a Chinese hospital show that the proposed approach can effectively identify meaningful treatment patterns from EMRs