Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

For more than a decade research in the field of context aware computing has aimed to find ways to exploit situational information that can be detected by mobile computing and sensor technologies. The goal is to provide people with new and improved applications, enhanced functionality and better use experience (Dey, 2001). Early applications focused on representing or computing on physical parameters, such as showing your location and the location of people or things around you. Such applications might show where the next bus is, which of your friends is in the vicinity and so on. With the advent of social networking software and microblogging sites such as Facebook and Twitter, recommender systems and so on context-aware computing is moving towards mining the social web in order to provide better representations and understanding of context, including social context. In this paper we begin by recapping different theoretical framings of context. We then discuss the problem of context- aware computing from a design perspective.

Towards semantic search and inference in electronic medical records : an approach using concept-based information retrieval

Background This paper presents a novel approach to searching electronic medical records that is based on concept matching rather than keyword matching. Aim The concept-based approach is intended to overcome specific challenges we identified in searching medical records. Method Queries and documents were transformed from their term-based originals into medical concepts as defined by the SNOMED-CT ontology. Results Evaluation on a real-world collection of medical records showed our concept-based approach outperformed a keyword baseline by 25% in Mean Average Precision. Conclusion The concept-based approach provides a framework for further development of inference based search systems for dealing with medical data.

Progress and challenges in the implementation of Electronic Medical Records in Saudi Arabia : a systematic review

Electronic Medical Record (EMR) systems are being implemented increasingly worldwide. Saudi Arabia is one of the developing countries that commenced implementing such systems in 1988. Whilst EMR uptake has been low in Saudi Arabia until now, a number of hospitals have implemented EMR systems successfully. This paper analyses available studies (n = 28) in the literature regarding EMR implementation in Saudi Arabia to identify the progress of EMR implementation to date and to identify the facilitators and barriers to implementation.

Agreement between self-report and medical records on signs and symptoms of respiratory illness

Background: Information on patient symptoms can be obtained by patient self-report or medical records review. Both methods have limitations. Aims: To assess the agreement between self-report and documentation in the medical records of signs/symptoms of respiratory illness (fever, cough, runny nose, sore throat, headache, sinus problems, muscle aches, fatigue, earache, and chills). Methods: Respondents were 176 research participants in the Hutterite Influenza Prevention Study during the 2008-2009 influenza season with information about the presence or absence of signs/symptoms from both self-report and primary care medical records. Results: Compared with medical records, lower proportions of self-reported fever, sore throat, earache, cough, and sinus problems were found. Total agreements between self-report and medical report of symptoms ranged from 61% (for sore throat) to 88% (for muscle aches and earache), with kappa estimates varying from 0.05 (for chills) to 0.41 (for cough) and 0.51 (for earache). Negative agreement was considerably higher (from 68% for sore throat to 93% for muscle aches and earache) than positive agreement (from 13% for chills to 58% for earache) for each symptom except cough where positive agreement (77%) was higher than negative agreement (64%). Agreements varied by age group. We found better agreement for earache (kappa=0.62) and lower agreements for headache, sinus problems, muscle aches, fatigue, and chills in older children (aged =5 years) and adults. Conclusions: Agreements were variable depending on the specific symptom. Contrary to research in other patient populations which suggests that clinicians report fewer symptoms than patients, we found that the medical record captured more symptoms than selfreport. Symptom agreement and disagreement may be affected by the perspectives of the person experiencing them, the observer, the symptoms themselves, measurement error, the setting in which the symptoms were observed and recorded, and the broader community and cultural context of patients. © 2012 Primary Care Respiratory Society UK. All rights reserved.

Access to medical records for research purposes:Varying perceptions across research ethics boards

Introduction: Variation across research ethics boards (REBs) in conditions placed on access to medical records for research purposes raises concerns around negative impacts on research quality and on human subject protection, including privacy. Aim: To study variation in REB consent requirements for retrospective chart review and who may have access to the medical record for data abstraction. Methods: Thirty 90-min face-to-face interviews were conducted with REB chairs and administrators affiliated with faculties of medicine in Canadian universities, using structured questions around a case study with open-ended responses. Interviews were recorded, transcribed and coded manually. Results: Fourteen sites (47%) required individual patient consent for the study to proceed as proposed. Three (10%) indicated that their response would depend on how potentially identifying variables would be managed. Eleven sites (38%) did not require consent. Two (7%) suggested a notification and opt-out process. Most stated that consent would be required if identifiable information was being abstracted from the record. Among those not requiring consent, there was substantial variation in recognising that the abstracted information could potentially indirectly re-identify individuals. Concern over access to medical records by an outside individual was also associated with requirement for consent. Eighteen sites (60%) required full committee review. Sixteen (53%) allowed an external research assistant to abstract information from the health record. Conclusions: Large variation was found across sites in the requirement for consent for research involving access to medical records. REBs need training in best practices for protecting privacy and confidentiality in health research. A forum for REB chairs to confidentially share concerns and decisions about specific studies could also reduce variation in decisions.

Understanding Continuance Intentions of Physicians with Electronic Medical Records (EMRS): An Expectancy-Confirmation Perspective

This thesis examines physicians’ satisfaction with electronic medical records (EMRs) in the post-adoption phase. More specifically, the study examines how physicians’ satisfaction with EMRs impacts on their intention to continue using as well as extend their adoption of additional functions of EMRs. Expectation-confirmation theory is used with the incorporation of perceived risk as the theoretical framework. The extended theoretical model is used to formulate eight hypotheses to aid in the understanding of physicians’ continuance intentions. A field survey of 135 Canadian physicians that utilize EMRs was performed to test the model empirically. The study found that physicians are willing to continue using and adopting additional components of EMRs. In addition, the empirical results suggest that physicians’ perceived usefulness and perceived risk impacts satisfaction, which in turn influences physicians’ continuance intentions. As well, perceived risk has an influence on physicians’ continuance intentions directly.

Extending feature usage: a study of the post-adoption of electronic medical records

Technology Acceptance Model (TAM) posits that Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) influence the ‘intention to use’. The Post-Acceptance Model (PAM) posits that continued use is influenced by prior experience. In order to study the factors that influence how professionals use complex systems, we create a tentative research model that builds on PAM and TAM. Specifically we include PEOU and the construct ‘Professional Association Guidance’. We postulate that feature usage is enhanced when professional associations influence PU by highlighting additional benefits. We explore the theory in the context of post-adoption use of Electronic Medical Records (EMRs) by primary care physicians in Ontario. The methodology can be extended to other professional environments and we suggest directions for future research.

Content validation for level of use of feature rich systems: a Delphi study of electronic medical records systems

Introduction. Feature usage is a pre-requisite to realising the benefits of investments in feature rich systems. We propose that conceptualising the dependent variable 'system use' as 'level of use' and specifying it as a formative construct has greater value for measuring the post-adoption use of feature rich systems. We then validate the content of the construct as a first step in developing a research instrument to measure it. The context of our study is the post-adoption use of electronic medical records (EMR) by primary care physicians. Method. Initially, a literature review of the empirical context defines the scope based on prior studies. Having identified core features from the literature, they are further refined with the help of experts in a consensus seeking process that follows the Delphi technique. Results.The methodology was successfully applied to EMRs, which were selected as an example of feature rich systems. A review of EMR usage and regulatory standards provided the feature input for the first round of the Delphi process. A panel of experts then reached consensus after four rounds, identifying ten task-based features that would be indicators of level of use. Conclusions. To study why some users deploy more advanced features than others, theories of post-adoption require a rich formative dependent variable that measures level of use. We have demonstrated that a context sensitive literature review followed by refinement through a consensus seeking process is a suitable methodology to validate the content of this dependent variable. This is the first step of instrument development prior to statistical confirmation with a larger sample.

The need for best practice standards in electronic governance of patient medical records to facilitate innovation

Background : Optimising the use of electronic data offers many opportunities to health services, particularly in rural and remote areas. These include reducing the effect of distance on access to clinical information and sharing information where there are multiple service providers for a single patient. The increasing compilation of large electronic databases of patient information and the ease with which electronic information can be transferred has raised concerns about the privacy and confidentiality of such records.
Aims & rationale/Objectives : This review aims to identify legal and ethical standards for areas of electronic governance where a lack of clarity may currently impede innovation in health service delivery.
Methods : This paper describes best practices for storage and transfer of electronic patient data based on an examination of Australian legislative requirements and a review of a number of current models. This will firstly allow us to identify basic legal requirements of electronic governance as well as areas of ambiguity not fully addressed by legislation. An examination of current models will suggest recommendations for best practice in areas lacking sufficient legal guidance.
Principal findings : We have identified the following four areas of importance, and shall discuss relevant details:
1) Patients' right of ownership to electronic patient records. 2) Custodial issues with data stored in centralised health care institutions 3) IT Security, including hierarchical level access, data encryption, data transfer standards and physical security 4) Software applications usage.
Discussion : Our examination of several models of best practice for the transfer of electronic patient data, both in Australia and internationally, identifies and clarifies many unresolved issues of electronic governance. This paper will also inform future policy in this area.
Implications : Clarification will facilitate the future development of beneficial technology-based innovations by rural health services.
Presentation type : Poster

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.