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The major impact on healthcare through the ongoing digital transformation and new technologies results in opportunities for improving quality of care. Electronic patient records (EPR) are a substantial part in this transformation, even though their influence on documentation remains often unclear. This review aims to answer the question of which effect the introduction of the EPR has on the documentation proper in hospitals. To do this, studies are reviewed that analyze the documentation itself, rather than merely conducting interviews or surveys about it. Several databases were searched in this systematic review (PubMed including PubMed, PubMed Central and Medline; PDQ Evidence; Web of Science Core Collection; CINHAL). To be included, studies needed to analyze written documentation and empirical data, be in either German or English language, published between 2010 and 2020, conducted in a hospital setting, focused on transition from paper-based to electronic patient records, and peer reviewed. Quantitative, qualitative and mixed methods studies were included. Studies were independently screened for inclusion by two researchers in three stages (title, abstract, full text) and, in case of disagreement, discussed with a third person from the research team until consensus was reached. The main outcome assessed was whether the studies indicated a negative or positive effect on documentation (e.g. changing the completeness of documentation) by introducing an EPR. Mixed Methods Appraisal Tool was used to assess the individual risk of bias in the included studies. Overall, 264 studies were found. Of these, 17 met the inclusion criteria and were included in this review. Of all included studies, 11 of 17 proved a positive effect of the introduction of the EPR on documentation such as an improved completeness or guideline adherence of the documentation. Six of 17 showed a mixed effect with positive and negative or no changes. No study showed an exclusively negative effect. Most studies found a positive effect of EPR introduction on documentation. However, it is difficult to draw specific conclusions about how the EPR affects or does not affect documentation since the included studies examined a variety of outcomes. As a result, various scenarios are conceivable with higher or reduced burden for practitioners. Additionally, the impact on treatment remains unclear.

Early identification of patients with life-threatening risks such as delirium is crucial in order to initiate preventive actions as quickly as possible. Despite intense research on machine learning for the prediction of clinical outcomes, the acceptance of the integration of such complex models in clinical routine remains unclear. The aim of this study was to evaluate user acceptance of an already implemented machine learning-based application predicting the risk of delirium for in-patients. We applied a mixed methods design to collect opinions and concerns from health care professionals including physicians and nurses who regularly used the application. The evaluation was framed by the Technology Acceptance Model assessing perceived ease of use, perceived usefulness, actual system use and output quality of the application. Questionnaire results from 47 nurses and physicians as well as qualitative results of four expert group meetings rated the overall usefulness of the delirium prediction positively. For healthcare professionals, the visualization and presented information was understandable, the application was easy to use and the additional information for delirium management was appreciated. The application did not increase their workload, but the actual system use was still low during the pilot study. Our study provides insights into the user acceptance of a machine learning-based application supporting delirium management in hospitals. In order to improve quality and safety in healthcare, computerized decision support should predict actionable events and be highly accepted by users. Supplementary Information The online version contains supplementary material available at 10.1007/s10916-021-01727-6.

Objectives Structured medical records improve readability and ensure the inclusion of information necessary for correct diagnosis and treatment. This is the first study to assess the quality of computer-generated structured medical records by comparing them to conventional medical records on patients with acute abdominal pain. Materials and methods A prospective double-blinded study was conducted in a tertiary referral center emergency department between January 2018 and June 2018. Patients were examined by emergency department physicians and by experience and inexperienced researcher. The researchers used a new electronical medical records system, which gathered data during the examination and the system generate structured medical records containing natural language. Conventional medical records dictated by physician and computer-generated medical records were compared by a group of independent clinicians. Results Ninety-nine patients were included. The overall quality of the computer-generated medical records was better than the quality of conventional human-generated medical records – the structure was similar or better in 99% of cases and the readability was similar or better in 86% of cases, p Discussion The quality of computer-generated structured medical records was superior to that of conventional medical records. The quality remained similar regardless of the researcher’s level of experience. The system allows automatic risk scoring and easy access for quality control of patient care. We therefore consider that it would be useful in wider practice.

Electronic health records (EHRs) present extensive patient information and may be used as a tool to improve health care. However, the oncology context presents a complex content that increases the difficulties of EHR application. This study aimed at developing openEHR-archetypes representing clinical concepts in cancer nutrition-care, as well as to develop an openEHR-template including the aforementioned archetypes. The study involved the following stages: 1) a thorough literature review, followed by an expert’s (nutrition guideline authors) survey, aiming to identify the main statements of published clinical guidelines on nutrition in cancer patients that were not included on the Clinical Knowledge Manager (CKM) repository; 2) modelling of the archetypes using the Ocean Archetype Software and submission to the CKM repository; 3) creating an example template with Template Designer; and 4) automatic conversion of the openEHR-template into a readily usable EHR using VCIntegrator. The clinical concepts (among 17 clinical concepts not yet available in the CKM repository) chosen for further development were: body composition, diet plan, dietary nutrients, dietary supplements, dietary intake assessment, and Malnutrition Screening Tool (MST). So far, four archetypes were accepted for review in the CKM repository and a template was created and converted into an EHR. This study designed new openEHR-archetypes for nutrition management in cancer patients. These archetypes can be included in EHR. Future studies are needed to assess their applicability in other areas and their practical impact on data quality, system interoperability and, ultimately, on clinical practice and research. Electronic supplementary material The online version of this article (10.1007/s10916-020-01649-9) contains supplementary material, which is available to authorized users.

Physiological signals can contain abundant personalized information and indicate health status and disease deterioration. However, in current medical practice, clinicians working in the general wards are usually lack of plentiful means and tools to continuously monitor the physiological signals of the inpatients. To address this problem, we here presented a medical-grade wireless monitoring system based on wearable and artificial intelligence technology. The system consists of a multi-sensor wearable device, database servers and user interfaces. It can monitor physiological signals such as electrocardiography and respiration and transmit data wirelessly. We highly integrated the system with the existing hospital information system and explored a set of processes of physiological signal acquisition, storage, analysis, and combination with electronic health records. Multi-scale information extracted from physiological signals and related to the deterioration or abnormality of patients could be shown on the user interfaces, while a variety of reports could be provided daily based on time-series signal processing technology and machine learning to make more information accessible to clinicians. Apart from an initial attempt to implement the system in a realistic clinical environment, we also conducted a preliminary validation of the core processes in the workflow. The heart rate veracity validation of 22 patient volunteers showed that the system had a great consistency with ECG Holter, and bias for heart rate was 0.04 (95% confidence interval: −7.34 to 7.42) beats per minute. The Bland-Altman analysis showed that 98.52% of the points were located between Mean ± 1.96SD. This system has been deployed in the general wards of the Hyperbaric Oxygen Department and Respiratory Medicine Department and has collected more than 1000 cases from the clinic. The whole system will continue to be updated based on clinical feedback. It has been demonstrated that this system can provide reliable physiological monitoring for patients in general wards and has the potential to generate more personalized pathophysiological information related to disease diagnosis and treatment from the continuously monitored physiological data.

Background Electronic Health Records (EHRs) can help clinicians to plan, document and deliver care for patients in healthcare services. When used consistently, EHRs can advance patient safety and quality, and reduce clinician’s workload. However, usability problems can make it difficult for clinicians to use EHRs effectively, which can negatively impact both healthcare professionals and patients. Objective To improve usability of EHRs within a mental health service in the UK. Methods This was a feasibility study conducted with two mental health teams. A mixed-methods approach was employed. Focus group discussions with clinicians identified existing usability problems in EHRs and changes were made to address these problems. Updated EHR assessment forms were evaluated by comparing the following measures pre and post changes: (1) usability testing to monitor time spent completing and duplicating patient information in EHRs, (2) clinician’s experience of using EHRs, and (3) proportion of completed EHR assessment forms. Results Usability testing with clinicians (n = 3) showed that the time taken to complete EHR assessment forms and time spent duplicating patient information decreased. Clinician’s experience of completing EHR assessment forms also significantly improved post changes compared to baseline (n = 71; p Conclusions Usability improvements to EHRs can reduce the time taken to complete forms, advance clinician’s experience and increase usage of EHRs. It is important to engage healthcare professionals in the usability improvement process of EHRs in mental health services.

Graph theory is a well-established theory with many methods used in mathematics to study graph structures. In the field of medicine, electronic health records (EHR) are commonly used to store and analyze patient data. Consequently, it seems straight-forward to perform research on modeling EHR data as graphs. This systematic literature review aims to investigate the frontiers of the current research in the field of graphs representing and processing patient data. We want to show, which areas of research in this context need further investigation. The databases MEDLINE, Web of Science, IEEE Xplore and ACM digital library were queried by using the search terms health record, graph and related terms. Based on the “Preferred Reporting Items for Systematic Reviews and Meta-Analysis” (PRISMA) statement guidelines the articles were screened and evaluated using full-text analysis. Eleven out of 383 articles found in systematic literature review were finally included for analysis in this literature review. Most of them use graphs to represent temporal relations, often representing the connection among laboratory data points. Only two papers report that the graph data were further processed by comparing the patient graphs using similarity measurements. Graphs representing individual patients are hardly used in research context, only eleven papers considered such kind of graphs in their investigations. The potential of graph theoretical algorithms, which are already well established, could help increasing this research field, but currently there are too few papers to estimate how this area of research will develop. Altogether, the use of such patient graphs could be a promising technique to develop decision support systems for diagnosis, medication or therapy of patients using similarity measurements or different kinds of analysis. Electronic supplementary material The online version of this article (10.1007/s10916-020-1538-4) contains supplementary material, which is available to authorized users.

Optimizing the workflow of a complex organization such as a hospital is a difficult task. An accurate option is to use a real-time locating system to track locations of both patients and staff. However, privacy regulations forbid hospital management to assess location data of their staff members. In this exploratory work, we propose a secure solution to analyze the joined location data of patients and staff, by means of an innovative cryptographic technique called Secure Multi-Party Computation, in which an additional entity that the staff members can trust, such as a labour union, takes care of the staff data. The hospital, owning location data of patients, and the labour union perform a two-party protocol, in which they securely cluster the staff members by means of the frequency of their patient facing times. We describe the secure solution in detail, and evaluate the performance of our proof-of-concept. This work thus demonstrates the feasibility of secure multi-party clustering in this setting.

The present study examined treatment pathways (the ordered sequence of medications that a patient is prescribed) for three chronic diseases (hypertension, type 2 diabetes, and depression), compared the pathways with recommendations from guidelines, discussed differences and standardization of medications in different medical institutions, explored population diversification and changes of clinical treatment, and provided clinical big data analysis-based data support for the development and study of drugs in China. In order to run the “Treatment Pathways in Chronic Disease” protocol in Chinese data sources,we have built a large data research and analysis platform for Chinese clinical medical data. Data sourced from the Clinical Data Repository (CDR) of the First Affiliated Hospital of Nanjing Medical University was extracted, transformed, and loaded into an observational medical outcomes partnership common data model (OMOP CDM) Ver. 5.0. Diagnosis and medication information for patients with hypertension, type 2 diabetes, and depression from 2005 to 2015 were extracted for observational research to obtain treatment pathways for the three diseases. The most common medications used to treat diabetes and hypertension were metformin and acarbose, respectively, at 28.5 and 20.9% as first-line medication. New drugs were emerging for depression; therefore, the favorite medication changed accordingly. Most patients with these three diseases had different treatment pathways from other patients with the same diseases. The proportions of monotherapy increased for the three diseases, especially in recent years. The recommendations presented in guidelines show some predominance. High-quality, effective guidelines incorporating domestic facts should be established to further guide medication and improve therapy at local hospitals. Medical institutions at all levels could improve the quality of medical services, and further standardize medications in the future. This research is the first application of the CDM model and OHDSI software in China, which were used to study, treatment pathways for three chronic diseases (hypertension, type 2 diabetes and depression), compare the pathways with recommendations from guidelines, discuss differences and standardization of medications in different medical institutions, demonstrate the urgent need for quality national guidelines, explores population diversification and changes of clinical treatment, and provide clinical big data analysis-based data support for the development and study of drugs in China.

With increased patient access to data, healthcare services are experiencing change where patients are moving away from being mere passive actors towards becoming more active and involved participants. In this paper, we explore the role of patient accessible electronic health records (PAEHRs) with respect to this increase in patient involvement. The study was performed as a case study and included nine interviews with patients and a survey that was responded to by 56 patients. Our results show that PAEHRs have a role in the enhancement of patient involvement because PAEHRs (i) foster a more balanced relationship between patients and healthcare professionals and (ii) increase access to information. Funding Agencies|Swedish Research School of Management and IT (MIT)

Electronic health records (EHRs) have emerged among health information technology as “meaningful use” to improve the quality and efficiency of healthcare, and health disparities in population health. In other instances, they have also shown lack of interoperability, functionality and many medical errors. With proper implementation and training, are electronic health records a viable source in managing population health? The primary objective of this systematic review is to assess the relationship of electronic health records’ use on population health through the identification and analysis of facilitators and barriers to its adoption for this purpose. Authors searched Cumulative Index of Nursing and Allied Health Literature (CINAHL) and MEDLINE (PubMed), 10/02/2012–10/02/2017, core clinical/academic journals, MEDLINE full text, English only, human species and evaluated the articles that were germane to our research objective. Each article was analyzed by multiple reviewers. Group members recognized common facilitators and barriers associated with EHRs effect on population health. A final list of articles was selected by the group after three consensus meetings (n = 55). Among a total of 26 factors identified, 63% (147/232) of those were facilitators and 37% (85/232) barriers. About 70% of the facilitators consisted of productivity/efficiency in EHRs occurring 33 times, increased quality and data management each occurring 19 times, surveillance occurring 17 times, and preventative care occurring 15 times. About 70% of the barriers consisted of missing data occurring 24 times, no standards (interoperability) occurring 13 times, productivity loss occurring 12 times, and technology too complex occurring 10 times. The analysis identified more facilitators than barriers to the use of the EHR to support public health. Wider adoption of the EHR and more comprehensive standards for interoperability will only enhance the ability for the EHR to support this important area of surveillance and disease prevention. This review identifies more facilitators than barriers to using the EHR to support public health, which implies a certain level of usability and acceptance to use the EHR in this manner. The public-health industry should combine their efforts with the interoperability projects to make the EHR both fully adopted and fully interoperable. This will greatly increase the availability, accuracy, and comprehensiveness of data across the country, which will enhance benchmarking and disease surveillance/prevention capabilities.

The healthcare data is an important asset and rich source of healthcare intellect. Medical databases, if created properly, will be large, complex, heterogeneous and time varying. The main challenge nowadays is to store and process this data efficiently so that it can benefit humans. Heterogeneity in the healthcare sector in the form of medical data is also considered to be one of the biggest challenges for researchers. Sometimes, this data is referred to as large-scale data or big data. Blockchain technology and the Cloud environment have proved their usability separately. Though these two technologies can be combined to enhance the exciting applications in healthcare industry. Blockchain is a highly secure and decentralized networking platform of multiple computers called nodes. It is changing the way medical information is being stored and shared. It makes the work easier, keeps an eye on the security and accuracy of the data and also reduces the cost of maintenance. A Blockchain-based platform is proposed that can be used for storing and managing electronic medical records in a Cloud environment.

Objectives: To enable better interoperability between Personal Health Record (PHR) and Electronic Health Record (EHR) systems to allow exchange of data from patients to providers and vice versa in order to encourage PHR use and patient self-management.\ud \ud Methods: A non-binding middleware based on open technologies and standards that resides between a PHR and EHR system has been developed. Specifically, the middleware consists of an ontology-driven information model based on the HL7 Reference Information Model (RIM) and a set of transformation rules that work in conjunction with the information model to process data exported from a PHR or EHR system and prepare it according to constraints imposed by the receiving system.\ud \ud Results: The information model was evaluated by executing a set of use case scenarios containing data exported from a PHR system, transformed according to the transformation rules, transferred to an EHR system and vice versa (EHR to PHR). This allowed various challenges to emerge as well as revealed gaps in current standards in use.\ud \ud Conclusions: The proposed middleware information model offers a number of advantages. When modifications are made to either a PHR or EHR system, they can be incorporated by altering only the instantiation of the information model. The model uses classes and attributes based on HL7 RIM to define how data is captured which allows greater flexibility in how data can be manipulated by receiving systems. The solution is applicable to existing PHR systems, or could be used as a blueprint to develop new PHR applications.

Prediction of major adverse kidney events in critically ill patients may help target therapy, allow risk adjustment, and facilitate the conduct of clinical trials. In a cohort comprised of all critically ill adults admitted to five intensive care units at a single tertiary care center over one year, we developed a logistic regression model for the outcome of Major Adverse Kidney Events within 30 days (MAKE30), the composite of persistent renal dysfunction, new renal replacement therapy (RRT), and in-hospital mortality. Proposed risk factors for the MAKE30 outcome were selected a priori and included age, race, gender, University Health System Consortium (UHC) expected mortality, baseline creatinine, volume of isotonic crystalloid fluid received in the prior 24 h, admission service, intensive care unit (ICU), source of admission, mechanical ventilation or receipt of vasopressors within 24 h of ICU admission, renal replacement therapy prior to ICU admission, acute kidney injury, chronic kidney disease as defined by baseline creatinine value, and renal failure as defined by the Elixhauser index. Among 10,983 patients in the study population, 1489 patients (13.6%) met the MAKE30 endpoint. The strongest independent predictors of MAKE30 were UHC expected mortality (OR 2.32 [95%CI 2.06---2.61]) and presence of acute kidney injury at ICU admission (OR 4.98 [95%CI 4.12---6.03]). The model had strong predictive properties including excellent discrimination with a bootstrap-corrected area-under-the-curve (AUC) of 0.903, and high precision of calibration with a mean absolute error prediction of 1.7%. The MAKE30 composite outcome can be reliably predicted from factors present within 24 h of ICU admission using data derived from the electronic health record.

The privacy of patients and the security of their information is the most imperative barrier to entry when considering the adoption of electronic health records in the healthcare industry. Considering current legal regulations, this review seeks to analyze and discuss prominent security techniques for healthcare organizations seeking to adopt a secure electronic health records system. Additionally, the researchers sought to establish a foundation for further research for security in the healthcare industry. The researchers utilized the Texas State University Library to gain access to three online databases: PubMed (MEDLINE), CINAHL, and ProQuest Nursing and Allied Health Source. These sources were used to conduct searches on literature concerning security of electronic health records containing several inclusion and exclusion criteria. Researchers collected and analyzed 25 journals and reviews discussing security of electronic health records, 20 of which mentioned specific security methods and techniques. The most frequently mentioned security measures and techniques are categorized into three themes: administrative, physical, and technical safeguards. The sensitive nature of the information contained within electronic health records has prompted the need for advanced security techniques that are able to put these worries at ease. It is imperative for security techniques to cover the vast threats that are present across the three pillars of healthcare.

The Epic electronic health record (EHR) platform supports structured data entry systems (SDES), which allow developers, with input from users, to create highly customized patient-record templates in order to maximize data completeness and to standardize structure. There are many potential advantages of using discrete data fields in the EHR to capture data for secondary analysis and epidemiological research, but direct data acquisition from clinicians remains one of the largest obstacles to leveraging the EHR for secondary use. Physician resistance to SDES is multifactorial. A 35-item questionnaire based on Unified Theory of Acceptance and Use of Technology, was used to measure attitudes, facilitation, and potential incentives for adopting SDES for clinical documentation among 25 pediatric specialty physicians and surgeons. Statistical analysis included chi-square for categorical data as well as independent sample t-tests and analysis of variance for continuous variables. Mean scores of the nine constructs demonstrated primarily positive physician attitudes toward SDES, while the surgeons were neutral. Those under 40 were more likely to respond that facilitating conditions for structured entry existed as compared to the two older age groups (p = .02). Pediatric surgeons were significantly less positive than specialty physicians about SDES effects on Performance (p = .01) and the effect of Social Influence (p = .02); but in more agreement that use of forms was voluntary (p = .02). Attitudinal differences likely reflect medical training, clinical practice workflows, and division specific practices. Identified resistance indicate efforts to increase SDES adoption should be discipline-targeted rather than a uniform approach.

There are lots of question-based data elements from the pharmacogenomics research network (PGRN) studies. Many data elements contain temporal information. To semantically represent these elements so that they can be machine processiable is a challenging problem for the following reasons: (1) the designers of these studies usually do not have the knowledge of any computer modeling and query languages, so that the original data elements usually are represented in spreadsheets in human languages; and (2) the time aspects in these data elements can be too complex to be represented faithfully in a machine-understandable way. In this paper, we introduce our efforts on representing these data elements using semantic web technologies. We have developed an ontology, CNTRO, for representing clinical events and their temporal relations in the web ontology language (OWL). Here we use CNTRO to represent the time aspects in the data elements. We have evaluated 720 time-related data elements from PGRN studies. We adapted and extended the knowledge representation requirements for EliXR-TIME to categorize our data elements. A CNTRO-based SPARQL query builder has been developed to customize users' own SPARQL queries for each knowledge representation requirement. The SPARQL query builder has been evaluated with a simulated EHR triple store to ensure its functionalities.

To obtain indications of the influence of electronic health records (EHR) in managing risks and meeting information system accreditation standard in Australian residential aged care (RAC) homes. The hypothesis to be tested is that the RAC homes using EHR have better performance in meeting information system standards in aged care accreditation than their counterparts only using paper records for information management. Content analysis of aged care accreditation reports from the Aged Care Standards and Accreditation Agency produced between April 2011 and December 2013. Items identified included types of information systems, compliance with accreditation standards, and indicators of failure to meet an expected outcome for information systems. The Chi-square test was used to identify difference between the RAC homes that used EHR systems and those that used paper records in not meeting aged care accreditation standards. 1,031 (37.4%) of 2,754 RAC homes had adopted EHR systems. Although the proportion of homes that met all accreditation standards was significantly higher for those with EHR than for homes with paper records, only 13 RAC homes did not meet one or more expected outcomes. 12 used paper records and nine of these failed the expected outcome for information systems. The overall contribution of EHR to meeting aged care accreditation standard in Australia was very small. Risk indicators for not meeting information system standard were no access to accurate and appropriate information, failure in monitoring mechanisms, not reporting clinical incidents, insufficient recording of residents' clinical changes, not providing accurate care plans, and communication processes failure. The study has provided indications that use of EHR provides small, yet significant advantages for RAC homes in Australia in managing risks for information management and in meeting accreditation requirements. The implication of the study for introducing technology innovation in RAC in Australia is discussed.

Acute kidney injury is common among critically ill adults and is associated with increased mortality and morbidity. The Major Adverse Kidney Events by 30 days (MAKE30) composite of death, new renal replacement therapy, or persistent renal dysfunction is recommended as a patient-centered outcome for pragmatic trials involving acute kidney injury. Accurate electronic detection of the MAKE30 endpoint using data within the electronic health record (EHR) could facilitate the use of the EHR in large-scale kidney injury research. In an observational study using prospectively collected data from 200 admissions to a single medical intensive care unit, we tested the performance of electronically-extracted data in identifying the MAKE30 composite compared to the reference standard of two-physician manual chart review. The incidence of MAKE30 on manual-review was 16 %, which included 8.5 % for in-hospital mortality, 3.5 % for new renal replacement therapy, and 8.5 % for persistent renal dysfunction. There was strong agreement between the electronic and manual assessment of MAKE30 (98.5 % agreement [95 % CI 96.5---100.0 %]; kappa 0.95 [95 % CI 0.87---1.00]; P?0.001), with only three patients misclassified by electronic assessment. Performance of the electronic MAKE30 assessment was similar among patients with and without CKD and with and without a measured serum creatinine in the 12 months prior to hospital admission. In summary, accurately identifying the MAKE30 composite outcome using EHR data collected as a part of routine care appears feasible.

Eighty four health care professionals participated in an online survey assessing the usability, and clinical and administrative impact of the Idaho Health Data Exchange's (IHDE) Virtual Health Record (VHR). The IHDE VHR allows authorized users to use a secure web interface to view lab, radiology and transcribed reports from multiple facilities and view medical histories on patients in the data exchange. Results indicate the usability of the IHDE VHR was almost universally positively rated with the Software Usability Measurement Inventory (SUMI) utilized as the assessment method. Medical providers however had the lowest rating of the exchange, raising concerns about the need for additional training and support. The addition of other Idaho health care organizations to the health data exchange was most widely desired, with the most frequently cited benefit being more comprehensive access to patients' records. In contrast to other published evaluations of health data exchanges in the U.S., few of the concerns emerged about cost of implementation of the data exchange or trust in the quality of information contained therein.

Surveillance Levels (SLs) are categories for medical patients (used in Brazil) that represent different types of medical recommendations. SLs are defined according to risk factors and the medical and developmental history of patients. Each SL is associated with specific educational and clinical measures. The objective of the present paper was to verify computer-aided, automatic assignment of SLs. The present paper proposes a computer-aided approach for automatic recommendation of SLs. The approach is based on the classification of information from patient electronic records. For this purpose, a software architecture composed of three layers was developed. The architecture is formed by a classification layer that includes a linguistic module and machine learning classification modules. The classification layer allows for the use of different classification methods, including the use of preprocessed, normalized language data drawn from the linguistic module. We report the verification and validation of the software architecture in a Brazilian pediatric healthcare institution. The results indicate that selection of attributes can have a great effect on the performance of the system. Nonetheless, our automatic recommendation of surveillance level can still benefit from improvements in processing procedures when the linguistic module is applied prior to classification. Results from our efforts can be applied to different types of medical systems. The results of systems supported by the framework presented in this paper may be used by healthcare and governmental institutions to improve healthcare services in terms of establishing preventive measures and alerting authorities about the possibility of an epidemic.

2011 has been a productive year for may hospitals that have gone through the conversion process to a fully integrated EMR. This is expensive and traumatic for the entire staff.We must now throttle back on the accelerator and let these systems settle and mature so that the bugs and problems can be resolved. England made serious mistakes in the pressure they placed on a central control of everything and it has cost them their investment. We need to learn from their experience and let innovation and experimentation be done on what is presently in the field so that we do not force inefficiency and error on those who follow. The companies need to spend less on connectivity and more on system improvement and physician modifications. Less regulatory control and more quality control are needed. Are these new systems really working and are they going to keep working?These questions need answers before more money is spent and perhaps wasted. Without the support of the staff and medical community, this whole investment will be wasted and the effort will be less than optimal. Let's hope sanity returns to Washington this year and we have an opportunity to study and analyze our current progress.

Patient records, including doctors' diagnoses of diseases, trace of treatments and patients' conditions, nursing actions, and examination results from allied health profession departments, are the most important medical records of patients in medical systems. With patient records, medical staff can instantly understand the entire medical information of a patient so that, according to the patient's conditions, more accurate diagnoses and more appropriate in-depth treatments can be provided. Nevertheless, in such a modern society with booming information technologies, traditional paper-based patient records have faced a lot of problems, such as lack of uniform formats, low data mobility, slow data transfer, illegible handwritings, enormous and insufficient storage space, difficulty of conservation, being easily damaged, and low transferability. To improve such drawbacks, reduce medical costs, and advance medical quality, paper-based patient records are modified into electronic medical records and reformed into electronic patient records. However, since electronic patient records used in various hospitals are diverse and different, in consideration of cost, it is rather difficult to establish a compatible and complete integrated electronic patient records system to unify patient records from heterogeneous systems in hospitals. Moreover, as the booming of the Internet, it is no longer necessary to build an integrated system. Instead, doctors can instantly look up patients' complete information through the Internet access to electronic patient records as well as avoid the above difficulties. Nonetheless, the major problem of accessing to electronic patient records cross-hospital systems exists in the security of transmitting and accessing to the records in case of unauthorized medical personnels intercepting or stealing the information. This study applies the Mobile Agent scheme to cope with the problem. Since a Mobile Agent is a program, which can move among hosts and automatically disperse arithmetic processes, and moves from one host to another in heterogeneous network systems with the characteristics of autonomy and mobility, decreasing network traffic, reducing transfer lag, encapsulating protocol, availability on heterogeneous platforms, fault-tolerance, high flexibility, and personalization. However, since a Mobile Agent contacts and exchanges information with other hosts or agents on the Internet for rapid exchange and access to medical information, the security is threatened. In order to solve the problem, this study proposes a key management scheme based on Lagrange interpolation formulas and hierarchical management structure to make Mobile Agents a more secure and efficient access control scheme for electronic patient record systems when applied to the access of patients' personal electronic patient records cross hospitals. Meanwhile, with the comparison of security and efficacy analyses being the feasibility of validation scheme and the basis of better efficiency, the security of Mobile Agents in the process of operation can be guaranteed, key management efficacy can be advanced, and the security of the Mobile Agent system can be protected.

Based on users' health issues, an intelligent personal health record (iPHR) system can automatically recommend home medical products (HMPs) and display them in a sequential order. However, the sequential output interface does not categorize search results and is not easy for users to quickly navigate to their desired HMPs. To address this problem, we developed a navigation interface for retrieved HMPs. Our idea is to use medical knowledge and nursing knowledge to construct a navigation hierarchy based on product categories. This hierarchy is added to the left side of each search result Web page to help users move through retrieved HMPs. We demonstrate the effectiveness of our techniques using USMLE medical exam cases.

Return on investment (ROI) concerns related to Electronic Health Records (EHRs) are a major barrier to the technology's adoption. Physicians generally rely upon early adopters to vet new technologies prior to putting them into widespread use. Therefore, early adopters' experiences with EHRs play a major role in determining future adoption patterns. The paper's purposes are: (1) to map the EHR value streams that define the ROI calculation; and (2) to compare Current Users' and Intended Adopters' perceived value streams to identify similarities, differences and governing constructs. Primary data was collected by the Texas Medical Association, which surveyed 1,772 physicians on their use and perceptions of practice gains from EHR adoption. Using Bayesian Belief Network Modeling, value streams are constructed for both current EHR users and Intended Adopters. Current Users and Intended Adopters differ significantly in their perceptions of the EHR value stream. Intended Adopters' value stream displays complex relationships among the potential gains compared to the simpler, linear relationship that Current Users identified. The Current Users identify "Reduced Medical Records Costs" as the gain that governs the value stream while Intended Adopters believe "Reduced Charge Capture Costs" define the value stream's starting point. Current Users' versus Intended Adopters' assessments of EHR benefits differ significantly and qualitatively from one another.

HL7 (Health Level 7) is a standard developed for exchanging incompatible healthcare information generated from programs or devices among heterogenous medical information systems. At present, HL7 is growing as a global standard. However, the HL7 standard does not support effective methods for treating data from various medical sensors, especially from mobile sensors. As ubiquitous systems are growing, HL7 must communicate with various medical transducers. In the area of sensor fields, IEEE 1451 is a group of standards for controlling transducers and for communicating data from/to various transducers. In this paper, we present the possibility of interoperability between the two standards, i.e., HL7 and IEEE 1451. After we present a method to integrate them and show the preliminary results of this approach.

Physicians in the U.S. are adopting electronic health records (EHRs) at an unprecedented rate. However, little is known about how EHR use relates to physicians' care decisions. Using nationally representative data, we estimated how using practice-based EHRs relates to opioid prescribing in primary care.This study analyzed 33,090 visits to primary care physicians (PCPs) in the 2007-2010 National Ambulatory Medical Care Survey. We used logistic regression to compare opioid prescribing by PCPs with and without EHRs.Thirteen percent of all visits and 33 % of visits for chronic noncancer pain resulted in an opioid prescription. Compared to visits without EHRs, visits to physicians with EHRs had 1.38 times the odds of an opioid prescription (95 % CI, 1.22-1.56). Among visits for chronic noncancer pain, physicians with EHRs had significantly higher odds of an opioid prescription (adj. OR = 1.39; 95 % CI, 1.03-1.88). Chronic pain visits involving electronic clinical notes were also more likely to result in an opioid prescription compared to chronic pain visits without (adj. OR = 1.51; 95 % CI, 1.10-2.05). Chronic pain visits involving electronic test ordering were also more likely to result in an opioid prescription compared to chronic pain visits without (adj. OR = 1.31; 95 % CI, 1.01-1.71).We found higher levels of opioid prescribing among physicians with EHRs compared to those without. These results highlight the need to better understand how using EHR systems may influence physician prescribing behavior so that EHRs can be designed to reliably guide physicians toward high quality care.

Heterogeneity in the management of the complex medical data, obstructs the attainment of data level interoperability among Health Information Systems (HIS). This diversity is dependent on the compliance of HISs with different healthcare standards. Its solution demands a mediation system for the accurate interpretation of data in different heterogeneous formats for achieving data interoperability. We propose an adaptive AdapteR Interoperability ENgine mediation system called ARIEN, that arbitrates between HISs compliant to different healthcare standards for accurate and seamless information exchange to achieve data interoperability. ARIEN stores the semantic mapping information between different standards in the Mediation Bridge Ontology (MBO) using ontology matching techniques. These mappings are provided by our System for Parallel Heterogeneity (SPHeRe) matching system and Personalized-Detailed Clinical Model (P-DCM) approach to guarantee accuracy of mappings. The realization of the effectiveness of the mappings stored in the MBO is evaluation of the accuracy in transformation process among different standard formats. We evaluated our proposed system with the transformation process of medical records between Clinical Document Architecture (CDA) and Virtual Medical Record (vMR) standards. The transformation process achieved over 90 % of accuracy level in conversion process between CDA and vMR standards using pattern oriented approach from the MBO. The proposed mediation system improves the overall communication process between HISs. It provides an accurate and seamless medical information exchange to ensure data interoperability and timely healthcare services to patients.

As you can see from a review of the physician interface, there are many exciting potentials that exist for the future of the EMR. None of these will happen unless we change the focus of the system design process and start by making the physician a key and primary player in this equation. To get a proactive physician following is the most important step forward and should be a primary focus for the large investments that are planned. We need a company or organization that sees the need for a future product that will accomplish these goals. We need funding for that process and a prototype development effort to perfect the design. We need the construction of a relational database of medicine with a professional staff to support its operation. Are there entrepreneurs still out there that can see this challenge and are willing to invest in its accomplishment? I sure hope so and I would be delighted to hear from you by email. Please send your comments and critique to: kqinc@aol.com

In primary care settings, follow-up regarding the outcome of acute outpatient visits is largely absent. We sought to develop an automated interactive voice response system (IVRS) for patient follow-up with feedback to providers capable of interfacing with multiple pre-existing electronic medical records (EMRs). A system was designed to extract data from EMRs, integrate with the IVRS, call patients for follow-up, and provide a feedback report to providers. Challenges during the development process were analyzed and summarized. The components of the technological solution and details of its implementation are reported. Lessons learned include: (1) Modular utilization of system components is often needed to adapt to specific clinic workflow and patient population needs (2) Understanding the local telephony environment greatly impacts development and is critical to success, and (3) Ample time for development of the IVRS questionnaire (mapping all branching paths) and speech recognition tuning (sensitivity, use of barge-in tuning, use of “known voice”) is needed. With proper attention to design and development, modular follow-up and feedback systems can be integrated into existing EMR systems providing the benefits of IVRS follow-up to patients and providers across diverse practice settings.

To improve the capability and usability of the personal health record (PHR) as a tool to empower consumers in the management of their own health, we have proposed the concept of an intelligent PHR (iPHR) and built a prototype iPHR system with four functions. These four functions use various health knowledge and computer science techniques to automatically provide users with personalized healthcare information to facilitate their well-being. This paper discusses several open issues in iPHR, including two enhancements to an existing function and two potential new functions. The two enhancements are for automatically compiling relevant self-care activities for each health issue and automatically identifying contraindicated self-care activities, respectively. One potential new function is personalized search for individual healthcare providers. Another potential new function is personalized local search for health-related services to help maintain patients in their homes. We include some preliminary thoughts on how to address these open issues with the hope to stimulate future research work on iPHR.

Although Web-based personal health records (PHRs) have been widely deployed, the existing ones have limited intelligence. Previously, we introduced expert system technology and Web search technology into the PHR domain and proposed the concept of an intelligent PHR (iPHR). iPHR provides personalized healthcare information to facilitate users' daily activities of living. The current iPHR is passive and follows the pull model of information distribution. This paper introduces triggers and monitoring into iPHR to make iPHR become active. Our idea is to let medical professionals pre-compile triggers and store them in iPHR's knowledge base. Each trigger corresponds to an abnormal event that may have potential medical impact. iPHR keeps collecting, processing, and analyzing the user's medical data from various sources such as wearable sensors. Whenever an abnormal event is detected from the user's medical data, the corresponding trigger fires and the related personalized healthcare information is pushed to the user using natural language generation technology, expert system technology, and Web search technology.

Current electronic health record (EHR) systems facilitate the storage, retrieval, persistence, and sharing of patient data. However, the way physicians interact with EHRs has not changed much. More specifically, support for temporal analysis of a large number of EHRs has been lacking. A number of information visualization techniques have been proposed to alleviate this problem. Unfortunately, due to their limited application to a single case study, the results are often difficult to generalize across medical scenarios. We present the usage data of Lifelines2 (Wang et al. 2008), our information visualization system, and user comments, both collected over eight different medical case studies. We generalize our experience into a visual analytics process model for multiple EHRs. Based on our analysis, we make seven design recommendations to information visualization tools to explore EHR systems.

Health care in the United States is rarely delivered in a coordinated manner. Current methods to share patient information are inefficient and may lead to medical errors, higher readmission rates, and delays in the delivery of needed health services. This qualitative study describes lessons learned concerning the early implementation of one Nationwide Health Information Network (NHIN) site in Long Beach, CA during its first year of operation. The Long Beach Network for Health (LBNH) focused on an incremental effort to exchange health information. Despite a limited concentration on emergency department care, virtually all respondents noted concerns regarding the sustainability, or business case, for the exchange of health information. Nevertheless, respondents were encouraged by progress on technological challenges and user requirements during this first year. The early gains in this process may, in turn, have laid the groundwork for future efforts to expand beyond the emergency department.

If Electronic Health Records contain a large amount of information about the patients condition and response to treatment, which can potentially revolutionize the clinical practice, such information is seldom considered due to the complexity of its extraction and analysis. We here report on a first integration of an NLP framework for the analysis of clinical records of lung cancer patients making use of a telephone assistance service of a major Spanish hospital. We specifically show how some relevant data, about patient demographics and health condition, can be extracted; and how some relevant analyses can be performed, aimed at improving the usefulness of the service. We thus demonstrate that the use of EHR texts, and their integration inside a data analysis framework, is technically feasible and worth of further study., 14 pages, 12 figures

Objective To test the hypothesis that advanced electronic medical record (EMR) capabilities are associated with better quality and safety of hospital care. Methods and Findings We used data from the HIMSS Analytics EMR Adoption Model (EMRAMSM) to measure the adoption and use of information technology in Dutch hospitals. To measure the quality and safety of healthcare in Dutch Hospitals we used select data from the publicly available basic set and the safety set of the Health Care Inspectorate (IGZ) and the Dutch Health Care Transparency Program ‘Zichtbare Zorg’ (ZIZO) program. The quality and safety measures selected reflect the measures used to score Dutch hospitals as presented in Elsevier’s annual ‘The Best Hospitals’ publication. The scores of this publication are based upon 542 of the 1516 available indicators from this basic set and safety set. Almost all indicators from the hospital-wide indicator sets are included in the selection, as are a large portion of indicators for acute care delivered by all hospitals. Of the 84 non-academic hospitals in the Netherlands, 67 (80 %) were included in this study. Results There is no statistically significant association found between a hospital’s EMRAM score and their overall quality/safety performance in the Elsevier hospital scoring model. Conclusion There is no evidence found to support the research hypothesis at this point in time. This outcome maybe the result of a multiplicity of factors to include the (limited) use of the methodologies used in this study, the fact that no fully digitalized hospital (EMRAM stage 7) is yet present in the NL, and/or the organizational competency of the NL hospitals in fully leveraging the EMR to facilitate patient care. Further research is needed to explore these findings.