Improved risk predictions via sparse imputation of patient conditions in electronic medical records

Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data - unlike many other datasets, EMR is sparse, reflecting the fact that patients have some, but not all diseases. We propose a novel model to fill-in these missing values, and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3 months admission prediction is improved significantly from (0.741 to 0.786) for Cancer data and (0.678 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting of multiple related risk outcomes (e.g. emergency presentations and admissions in hospital over 3, 6 and 12 months period) in an integrated framework. For this model, the AUC averaged over outcomes is improved significantly from (0.768 to 0.806) for Cancer data and (0.685 to 0.748) for AMI data.

Cost-effectiveness analysis of a hospital electronic medication management system

OBJECTIVE: To conduct a cost-effectiveness analysis of a hospital electronic medication management system (eMMS). METHODS: We compared costs and benefits of paper-based prescribing with a commercial eMMS (CSC MedChart) on one cardiology ward in a major 326-bed teaching hospital, assuming a 15-year time horizon and a health system perspective. The eMMS implementation and operating costs were obtained from the study site. We used data on eMMS effectiveness in reducing potential adverse drug events (ADEs), and potential ADEs intercepted, based on review of 1 202 patient charts before (n = 801) and after (n = 401) eMMS. These were combined with published estimates of actual ADEs and their costs. RESULTS: The rate of potential ADEs following eMMS fell from 0.17 per admission to 0.05; a reduction of 71%. The annualized eMMS implementation, maintenance, and operating costs for the cardiology ward were A$61 741 (US$55 296). The estimated reduction in ADEs post eMMS was approximately 80 actual ADEs per year. The reduced costs associated with these ADEs were more than sufficient to offset the costs of the eMMS. Estimated savings resulting from eMMS implementation were A$63-66 (US$56-59) per admission (A$97 740-$102 000 per annum for this ward). Sensitivity analyses demonstrated results were robust when both eMMS effectiveness and costs of actual ADEs were varied substantially. CONCLUSION: The eMMS within this setting was more effective and less expensive than paper-based prescribing. Comparison with the few previous full economic evaluations available suggests a marked improvement in the cost-effectiveness of eMMS, largely driven by increased effectiveness of contemporary eMMs in reducing medication errors.

Hierarchical Bayesian nonparametric models for knowledge discovery from electronic medical records

Electronic Medical Record (EMR) has established itself as a valuable resource for large scale analysis of health data. A hospital EMR dataset typically consists of medical records of hospitalized patients. A medical record contains diagnostic information (diagnosis codes), procedures performed (procedure codes) and admission details. Traditional topic models, such as latent Dirichlet allocation (LDA) and hierarchical Dirichlet process (HDP), can be employed to discover disease topics from EMR data by treating patients as documents and diagnosis codes as words. This topic modeling helps to understand the constitution of patient diseases and offers a tool for better planning of treatment. In this paper, we propose a novel and flexible hierarchical Bayesian nonparametric model, the word distance dependent Chinese restaurant franchise (wddCRF), which incorporates word-to-word distances to discover semantically-coherent disease topics. We are motivated by the fact that diagnosis codes are connected in the form of ICD-10 tree structure which presents semantic relationships between codes. We exploit a decay function to incorporate distances between words at the bottom level of wddCRF. Efficient inference is derived for the wddCRF by using MCMC technique. Furthermore, since procedure codes are often correlated with diagnosis codes, we develop the correspondence wddCRF (Corr-wddCRF) to explore conditional relationships of procedure codes for a given disease pattern. Efficient collapsed Gibbs sampling is derived for the Corr-wddCRF. We evaluate the proposed models on two real-world medical datasets - PolyVascular disease and Acute Myocardial Infarction disease. We demonstrate that the Corr-wddCRF model discovers more coherent topics than the Corr-HDP. We also use disease topic proportions as new features and show that using features from the Corr-wddCRF outperforms the baselines on 14-days readmission prediction. Beside these, the prediction for procedure codes based on the Corr-wddCRF also shows considerable accuracy.

The personally controlled electronic health record (PCEHR) for adults with severe communication impairments: findings of pilot research.

To date, there is little information in the literature to guide the provision of supports for using the Personally Controlled Electronic Health Record (PCEHR) in populations with severe communication impairments associated with a range of disabilities. In this paper we will (a) outline the rationale for use of PCEHR in these populations by providing an overview of relevant research to date, and (b) present results of three integrated pilot studies aiming to investigate the barriers to and facilitators for PCEHR use by people with severe communication impairments and their service providers. Finally, we will present directions for future research on use of PCEHR by people with severe communication impairments.

The Syrian electronic army – a hacktivist group

Purpose – The aim of the paper is to assess the hacktivist group called the Syrian Electronic Army and determine what their motivations in terms of ethical and poetical motivations.
Design/methodology/approach – This paper looks at chronological examples of Syrian Electronic Army activities and assess them using a developed hacktivist criteria to try and gain a greater understanding of the motivations of the Syrian Electronic Army. The paper uses a netnography research approach.
Findings – This paper determines that the Syrian Electronic Army is motivated to protect the Syrian Government. This protection is highlighted by the new media and social media organisations that theSyrian Electronic Army attacks online.
Research limitations/implications – This paper focuses only on one group the Syrian Electronic Army.
Practical implications – A greater understanding of the Syrian Electronic Army.
Social implications – A greater understanding of the development of hacktivism.
Originality/value – A unique study into the motivation of the Syrian Electronic Army.

Financial model for investment recovery period in electronic health records implementations

Technology usage for better healthcare delivery is being emphasised in the USA and other advanced nations. Electronic health records (EHR) are being widely seen as improving operational efficiency and reducing medication errors in clinic practices and hospitals. Further, hospitals and clinics stand to gain incentives from the federal government if they implement EHRs and demonstrate meaningful use of EHRs. While numerous other aspects of HER implementations is found in literature, financial models have not been well studied. Before implementing EHR, one must take into consideration investment recovery period considering the costs, savings and possible tax incentives. In this paper, we develop financial model for computing investment recovery period in EHR implementations assuming constant patient visits. We further develop required growth rate formula if investments need to be recovered in fixed number of years. The model is illustrated with numerical example.

Fabrication of biocompatible enclosures for an electronic implant using 3D printing

A variety of different approaches have been employed to enableimplantation of electronic medical microdevices. A novel method of producing low-cost, rapidly fabricated implantable enclosures from biocompatible silicone is presented in this paper. This method utilises 3D computer-aided design software to design and model the enclosures prior to fabrication. The enclosures are then fabricated through additive manufacturing from biocompatible silicone using a 3D bioprinter. In this paper, four different implantable enclosure designs are presented. A prototyping stage with three different prototypes is described, these prototype enclosures are then evaluated through submersion and operation tests. A final design is developed in response to the obtained results, and then evaluated in a long term temperature controlled submersion test. The evaluation results are presented and discussed.Several areas of future works are identified and discussed.