Hadoop for EEG Storage and Processing: A Feasibility Study

Lots of heterogeneous complex data are collected for diagnosis purposes. Such data should be shared between all caregivers and, often at least partly automatically processed, due to its complexity, for its full potential to be harnessed. This paper is a feasibility study that assesses the potential of Hadoop as a medical data storage and processing platform using EEGs as example of medical data

The vulnerabilities of computerized physician order entry systems: a qualitative study

Objective To test the vulnerabilities of a wide range of computerized physician order entry (CPOE) systems to different types of medication errors, and develop a more comprehensive qualitative understanding of how their design could be improved. Materials and Methods The authors reviewed a random sample of 63 040 medication error reports from the US Pharmacopeia (USP) MEDMARX reporting system where CPOE systems were considered a “contributing factor” to errors and flagged test scenarios that could be tested in current CPOE systems. Testers entered these orders in 13 commercial and homegrown CPOE systems across 16 different sites in the United States and Canada, using both usual practice and where-needed workarounds. Overarching themes relevant to interface design and usability/workflow issues were identified. Results CPOE systems often failed to detect and prevent important medication errors. Generation of electronic alert warnings varied widely between systems, and depended on a number of factors, including how the order information was entered. Alerts were often confusing, with unrelated warnings appearing on the same screen as those more relevant to the current erroneous entry. Dangerous drug-drug interaction warnings were displayed only after the order was placed rather than at the time of ordering. Testers illustrated various workarounds that allowed them to enter these erroneous orders. Discussion and Conclusion The authors found high variability in ordering approaches between different CPOE systems, with major deficiencies identified in some systems. It is important that developers reflect on these findings and build in safeguards to ensure safer prescribing for patients

Spanning forests and the q-state Potts model in the limit q \to 0

We study the q-state Potts model with nearest-neighbor coupling v=e^{\beta J}-1 in the limit q,v \to 0 with the ratio w = v/q held fixed. Combinatorially, this limit gives rise to the generating polynomial of spanning forests; physically, it provides information about the Potts-model phase diagram in the neighborhood of (q,v) = (0,0). We have studied this model on the square and triangular lattices, using a transfer-matrix approach at both real and complex values of w. For both lattices, we have computed the symbolic transfer matrices for cylindrical strips of widths 2 \le L \le 10, as well as the limiting curves of partition-function zeros in the complex w-plane. For real w, we find two distinct phases separated by a transition point w=w_0, where w_0 = -1/4 (resp. w_0 = -0.1753 \pm 0.0002) for the square (resp. triangular) lattice. For w > w_0 we find a non-critical disordered phase, while for w < w_0 our results are compatible with a massless Berker-Kadanoff phase with conformal charge c = -2 and leading thermal scaling dimension x_{T,1} = 2 (marginal operator). At w = w_0 we find a "first-order critical point": the first derivative of the free energy is discontinuous at w_0, while the correlation length diverges as w \downarrow w_0 (and is infinite at w = w_0). The critical behavior at w = w_0 seems to be the same for both lattices and it differs from that of the Berker-Kadanoff phase: our results suggest that the conformal charge is c = -1, the leading thermal scaling dimension is x_{T,1} = 0, and the critical exponents are \nu = 1/d = 1/2 and \alpha = 1.Comment: 131 pages (LaTeX2e). Includes tex file, three sty files, and 65 Postscript figures. Also included are Mathematica files forests_sq_2-9P.m and forests_tri_2-9P.m. Final journal versio

Advancing the research agenda for diagnostic error reduction

Diagnostic errors remain an underemphasised and understudied area of patient safety research. We briefly summarise the methods that have been used to conduct research on epidemiology, contributing factors and interventions related to diagnostic error and outline directions for future research. Research methods that have studied epidemiology of diagnostic error provide some estimate on diagnostic error rates. However, there appears to be a large variability in the reported rates due to the heterogeneity of definitions and study methods used. Thus, future methods should focus on obtaining more precise estimates in different settings of care. This would lay the foundation for measuring error rates over time to evaluate improvements. Research methods have studied contributing factors for diagnostic error in both naturalistic and experimental settings. Both approaches have revealed important and complementary information. Newer conceptual models from outside healthcare are needed to advance the depth and rigour of analysis of systems and cognitive insights of causes of error. While the literature has suggested many potentially fruitful interventions for reducing diagnostic errors, most have not been systematically evaluated and/or widely implemented in practice. Research is needed to study promising intervention areas such as enhanced patient involvement in diagnosis, improving diagnosis through the use of electronic tools and identification and reduction of specific diagnostic process ‘pitfalls’ (eg, failure to conduct appropriate diagnostic evaluation of a breast lump after a ‘normal’ mammogram). The last decade of research on diagnostic error has made promising steps and laid a foundation for more rigorous methods to advance the field

Predicting Inpatient Medication Orders From Electronic Health Record Data.

In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train two machine-learning models: A deep learning sequence model and a logistic regression model. Both were compared with a baseline that ranked the most frequently ordered medications based on a patient's discharge hospital service and amount of time since admission. Models were trained to predict from 990 possible medications at the time of order entry. Fifty-five percent of medications ordered by physicians were ranked in the sequence model's top-10 predictions (logistic model: 49%) and 75% ranked in the top-25 (logistic model: 69%). Ninety-three percent of the sequence model's top-10 prediction sets contained at least one medication that physicians ordered within the next day. These findings demonstrate that medication orders can be predicted from information present in the EHR

The limited acceptance of an electronic prescription system by general practitioners: reasons and practical implications

To control the cost of drugs prescribed by general practitioners (GPs), the Netherlands Ministry of Health decided to implement an electronic prescription system. This paper uses an interpretive perspective to analyse the reasons for limited acceptance of the system. While the promotion campaign focused on the system, GPs based their decision on wider contextual factors

Meaningful use of electronic health records: experiences from the field and future opportunities

Background: With the aim of improving health care processes through health information technology (HIT), the US government has promulgated requirements for “meaningful use” (MU) of electronic health records (EHRs) as a condition for providers receiving financial incentives for the adoption and use of these systems. Considerable uncertainty remains about the impact of these requirements on the effective application of EHR systems. Objective: The Agency for Healthcare Research and Quality (AHRQ)-sponsored Centers for Education and Research in Therapeutics (CERTs) critically examined the impact of the MU policy relating to the use of medications and jointly developed recommendations to help inform future HIT policy. Methods: We gathered perspectives from a wide range of stakeholders (N=35) who had experience with MU requirements, including academicians, practitioners, and policy makers from different health care organizations including and beyond the CERTs. Specific issues and recommendations were discussed and agreed on as a group. Results: Stakeholders’ knowledge and experiences from implementing MU requirements fell into 6 domains: (1) accuracy of medication lists and medication reconciliation, (2) problem list accuracy and the shift in HIT priorities, (3) accuracy of allergy lists and allergy-related standards development, (4) support of safer and effective prescribing for children, (5) considerations for rural communities, and (6) general issues with achieving MU. Standards are needed to better facilitate the exchange of data elements between health care settings. Several organizations felt that their preoccupation with fulfilling MU requirements stifled innovation. Greater emphasis should be placed on local HIT configurations that better address population health care needs. Conclusions: Although MU has stimulated adoption of EHRs, its effects on quality and safety remain uncertain. Stakeholders felt that MU requirements should be more flexible and recognize that integrated models may achieve information-sharing goals in alternate ways. Future certification rules and requirements should enhance EHR functionalities critical for safer prescribing of medications in children