Designing a Coding Data Warehouse to Support and Enhance Medical Act Coding.

Standards Data Warehouse has been implemented in many hospitals. It has enormous potential to improve performance measurement and health care quality. Accessing, organizing, and using these data to optimize clinical coding are evolving challenges for hospital systems. This paper describes development of a coding data warehouse based Entities-Attribute-Value (EAV) that we created by importing data from the clinical data warehouse (CDW) of public hospital. In particular, it focuses on design, implementation, and evaluation of the warehouse. Moreover, it defines the rules to convert a conceptual model of coding into an EAV logical model and his implementation using integrating biology and the bedside (i2b2). We evaluate it using data research mono and multi-criteria and then calculate the precision of our model. The result shows that, the coding data warehouse provides with good accuracy, an association of diagnostic code and medical act closer the patient's clinical landscape. Doctors without knowledge of coding rules could use this information to optimize and improve the diagnostic coding.

Design and Implementation of Big Data-Based Documents to Optimize Medical Coding.

Clinical information systems (CISs) in some hospitals streamline the data management from data warehouses. These warehouses contain heterogeneous information from all medical specialties that offer patient care services. It is increasingly difficult to manage large volumes of data in a specific clinical context such as quality coding of medical services. The document-based Not Only SQL (NO-SQL) model can provide an accessible, extensive and robust coding data management framework while maintaining certain flexibility. This paper focus on the design and implementation of a big data-coding warehouse, it also defines the rules to convert a conceptual model of coding into a document-oriented logical model. Using that model, we implemented, analyzed a big data-coding warehouse via the Mongodb database, and evaluated it using data research mono- and multi-criteria and then calculated the precision of our model.

Constructing a Pre-Emptive System Based on a Multidimentional Matrix and Autocompletion to Improve Diagnostic Coding in Acute Care Hospitals.

Short-stay MSO (Medicine, Surgery, Obstetrics) hospitalization activities in public and private hospitals providing public services are funded through charges for the services provided (T2A in French). Coding must be well matched to the severity of the patient's condition, to ensure that appropriate funding is provided to the hospital. We propose the use of an autocompletion process and multidimensional matrix, to help physicians to improve the expression of information and to optimize clinical coding. With this approach, physicians without knowledge of the encoding rules begin from a rough concept, which is gradually refined through semantic proximity and uses information on the associated codes stemming of optimized knowledge bases of diagnosis code.

Using a snowflake data model and autocompletion to support diagnostic coding in acute care hospitals.

PURPOSE: Efficient and adequate coding is essential for all hospitals to optimize funding, follow activity, and perform epidemiological studies. OBJECTIVE: We propose an autocompletion method for optimizing diagnostic coding in acute care hospitals. METHODS: Using a terminology snowflake model integrating SNOMED 3.5 and ICD-10 codes, autocompletion algorithms generate a list of diagnostic expressions from partial input concepts. RESULTS: A general autocompletion component has been developed and tested on a set of inpatient summary reports. Concepts expressed as strings of three or four characters return a noisy list of diagnostic labels or codes. Concepts expressed as groups of strings return lists that are semantically close to the labels present in hospital reports. The most pertinent information lies in the length of the expressions entered. CONCLUSION: Autocompletion can be a complementary tool to existing coding support systems.

Computing the compliance of physician drug orders with guidelines using an OWL2 reasoner and standard drug resources.

Assessing the conformity of a physician's prescription to a given recommended prescription is not obvious since both prescriptions are expressed at different levels of abstraction and may concern only a subpart of the whole order. Recent formalisms (OWL2) and tools (reasoners) from the semantic web technologies are becoming available to represent defined concepts and to handle classification services. We propose a generic framework based on such technologies, using available standardized drug resources, to compute the compliance of a given drug order to a recommended prescription, such that the subsumption relationship yields the conformity relationship between the order and the recommendation. The ATC drug classification has been used as a local ontology. The method has been successfully implemented for arterial hypertension management for which we had a sample of antihypertensive orders. However, supplemental standardized drug knowledge is needed to correctly compare drug orders to recommended orders.