Modern Electronic Health Records Research Articles

Real world challenges in maintaining data integrity in electronic health records in a cancer program

Electronic Health Record (EHR) systems increase clerical workload, promote copy-paste and error propagation. Documentation error rate in cancer diagnosis and treatment was examined in 776 patient records. Fifteen percent of the charts contained an error. Modern EHR systems, patient portals and engagement tools may facilitate the maintenance of accurate information.

A Comparison of Resident Procedure Logs to Data Generated from an Electronic Health Record

Abstract Background Emergency medicine (EM) residents are required to report procedural competency, often in a database separate from the electronic health record (EHR), in a redundant and time-consuming manner. We hypothesize that, if documented in an appropriate manner, procedural reports generated from an EHR reliably exceed those required by the Accreditation Council for Graduate Medical Education (ACGME) as well as those self-reported by EM residents. Objectives (1) To compare the number of medical resuscitations recorded in the EHR to the number documented by residents in a separate database. (2) To compare the number of medical resuscitations recorded in the EHR to the ACGME requirement for graduation. Methods Self-reported numbers of adult medical resuscitation by each resident of the previous three graduating classes of one EM program were compared with those generated from the EHR (Epic Systems, Verona, Wisconsin). There is no discrete documentation of medical resuscitations in the EHR. The ACGME describes a resuscitation as “…patient care for which prolonged physician attention is needed,” and thus, a surrogate was determined to be any patient for which the attending physician documented critical care time. Results Data generated from the EHR reliably exceeded reported (mean [M] = 165.78, standard deviation [SD] = 45.97) and required (M = 188.09, SD = 30.93) numbers for adult medical resuscitations for 100% of the residents of the past three graduating classes (n = 32). Conclusion In an accredited EM residency program that utilizes a modern EHR with a validated reporting functionality, residents should not need to redundantly log the number of adult medical resuscitations performed. Each resident in this study performed significantly more adult medical resuscitations than they logged and more than required by the ACGME, and thus, the time spent documenting these in a separate database was superfluous. Furthermore, this process increases resident awareness of proper documentation and data stewardship, two skills certain to prevail throughout their careers as modern EM physicians.

SAP BusinessObjects in Medical Informatics.

Electronic health record (EHR) generates a large amount of data filled with opportunities to enhance documentation compliance, quality improvement, and other metrics. Various software tools exist, but many clinicians are unaware of them. Our institution switched from a hybrid of paper and multiple small EHRs to one all-inclusive EHR system. We faced significant challenges beyond the typical new software deployment phase that affected our departmental regulatory compliance, quality measures, and research initiatives. We aimed to navigate these issues through the use of medical informatics. We used a multidimensional database software analysis tool called SAP BusinessObjects® (SAP SE. Released 2020. SAP BusinessObjects, Version 14.2.8.3671. Waldorf, Germany) to design automated queries for the patient database to generate various reports for our department. As a result, We improved our anesthesia documentation non-compliance from 13-17% of all cases to 4% within months. We have also used this tool to automatically generate various reports such as preoperative beta-blocker administrations, caseloads, case complications, procedure logs, and medication records. Even today many departments rely on manual checks for even the most basic documentation and quality metric compliance, which can be time consuming and costly. Using medical informatics tools is a highly efficient alternative. Fortunately, many software tools exist within most modern EHR packages, and most people can learn to use these tools productively.

Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation.

Reducing care lapses for people living with HIV is critical to ending the HIV epidemic and beneficial for their health. Predictive modeling can identify clinical factors associated with HIV care lapses. Previous studies have identified these factors within a single clinic or using a national network of clinics, but public health strategies to improve retention in care in the United States often occur within a regional jurisdiction (eg, a city or county). We sought to build predictive models of HIV care lapses using a large, multisite, noncurated database of electronic health records (EHRs) in Chicago, Illinois. We used 2011-2019 data from the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a database including multiple health systems, covering the majority of 23,580 people with an HIV diagnosis living in Chicago. CAPriCORN uses a hash-based data deduplication method to follow people across multiple Chicago health care systems with different EHRs, providing a unique citywide view of retention in HIV care. From the database, we used diagnosis codes, medications, laboratory tests, demographics, and encounter information to build predictive models. Our primary outcome was lapses in HIV care, defined as having more than 12 months between subsequent HIV care encounters. We built logistic regression, random forest, elastic net logistic regression, and XGBoost models using all variables and compared their performance to a baseline logistic regression model containing only demographics and retention history. We included people living with HIV with at least 2 HIV care encounters in the database, yielding 16,930 people living with HIV with 191,492 encounters. All models outperformed the baseline logistic regression model, with the most improvement from the XGBoost model (area under the receiver operating characteristic curve 0.776, 95% CI 0.768-0.784 vs 0.674, 95% CI 0.664-0.683; P<.001). Top predictors included the history of care lapses, being seen by an infectious disease provider (vs a primary care provider), site of care, Hispanic ethnicity, and previous HIV laboratory testing. The random forest model (area under the receiver operating characteristic curve 0.751, 95% CI 0.742-0.759) revealed age, insurance type, and chronic comorbidities (eg, hypertension), as important variables in predicting a care lapse. We used a real-world approach to leverage the full scope of data available in modern EHRs to predict HIV care lapses. Our findings reinforce previously known factors, such as the history of prior care lapses, while also showing the importance of laboratory testing, chronic comorbidities, sociodemographic characteristics, and clinic-specific factors for predicting care lapses for people living with HIV in Chicago. We provide a framework for others to use data from multiple different health care systems within a single city to examine lapses in care using EHR data, which will aid in jurisdictional efforts to improve retention in HIV care.

Using Clinical Data Visualizations in Electronic Health Record User Interfaces to Enhance Medical Student Diagnostic Reasoning: Randomized Experiment.

In medicine, the clinical decision-making process can be described using the dual-process theory consisting of the fast, intuitive "System 1," commonly seen in seasoned physicians, and the slow, deliberative "System 2," associated with medical students. System-1-type diagnostic reasoning is thought to be less cognitively burdensome, thereby reducing physician error. To date, limited literature exists on inducing System-1-type diagnosis in medical students through cognitive heuristics, particularly while using modern electronic health record (EHR) interfaces. In this experimental pilot study, we aimed to (1) attempt to induce System-1-type diagnostic reasoning in inexperienced medical students through the acquisition of cognitive user interface heuristics and (2) understand the impact of clinical patient data visualizations on students' cognitive load and medical education. The participants were third- and fourth-year medical students recruited from the University of Pittsburgh School of Medicine who had completed 1+ clinical rotations. The students were presented 8 patient cases on a novel EHR, featuring a prominent data visualization designed to foster at-a-glance rapid case assessment, and asked to diagnose the patient. Half of the participants were shown 4 of the 8 cases repeatedly, up to 4 times with 30 seconds per case (Group A), and the other half of the participants were shown cases twice with 2 minutes per case (Group B). All participants were then asked to provide full diagnoses of all 8 cases. Finally, the participants were asked to evaluate and elaborate on their experience with the system; content analysis was subsequently performed on these user experience interviews. A total of 15 students participated. The participants in Group A scored slightly higher on average than those in Group B, with a mean percentage correct of 76% (95% CI 0.68-0.84) versus 69% (95% CI 0.58-0.80), and spent on average 50% less time per question than Group B diagnosing patients (13.98 seconds vs 19.13 seconds, P=.03, respectively). When comparing the novel EHR design to previously used EHRs, 73% (n=11) of participants rated the new version on par or higher (3+/5). Ease of use and intuitiveness of this new system rated similarly high (mean score 3.73/5 and 4.2/5, respectively). In qualitative thematic analysis of poststudy interviews, most participants (n=11, 73%) spoke to "pattern-recognition" cognitive heuristic strategies consistent with System 1 decision-making. These results support the possibility of inducing type-1 diagnostics in learners and the potential for data visualization and user design heuristics to reduce cognitive burden in clinical settings. Clinical data presentation in the diagnostic reasoning process is ripe for innovation, and further research is needed to explore the benefit of using such visualizations in medical education.

Integrated rapid-cycle comparative effectiveness trials using flexible point of care randomisation in electronic health record systems

Whilst the Randomised Controlled Trial remains the gold standard for deriving robust causal estimates of treatment efficacy, too often a traditional design proves prohibitively expensive or cumbersome when it comes to assessing questions regarding the comparative effectiveness of routinely used treatments. As a result, patients experience variation in practice as clinicians lack the evidence needed to personalise treatments effectively. This variation may be classified as unwarranted, where existing evidence is ignored, or legitimate where in the absence of evidence, clinicians rely on experience, expert opinion, and inferred principles from basic science to make decisions.We argue that within the right ethical and technological framework, legitimate variation can be transformed into a mechanism for evidence generation and learning. Learning Health Systems which harness existing variation in practice, represent a novel approach for generating evidence from everyday clinical practice. The development of these systems has gained traction due to the increased availability of modern Electronic Health Record Systems. However, despite their promise, overcoming hurdles to successfully integrating clinical trials within Learning Health Systems has proven challenging.This article describes the origins of integrated clinical trials and explores two main barriers to their further implementation - how best to obtain informed consent from patients to participate in routine comparative effectiveness research, and how to automate and integrate randomisation into a clinical workflow. Having described these barriers, we present a potential solution in the form of a research pipeline using a novel form of flexible point-of-care randomisation to allow clinicians and patients to participate in studies where there is clinical equipoise.

Epidural hematoma in patients with ankylosing spondylitis requiring surgical stabilization: a single-institution retrospective review with literature analysis.

Ankylosing spondylitis (AS) is a chronic inflammatory disease affecting the sacroiliac joints and axial spine that is closely linked with human leukocyte antigen-B27. There appears to be an increased frequency of associated epidural hematomas in spine fractures in patients with AS. The objective was to review the incidence within the literature and a single-institution experience of the occurrence of epidural hematoma in the context of patients with AS requiring spine surgery. Deep 6 AI software was used to search the entire database of patients at a single level I trauma center (since the advent of the institution's modern electronic health record system) to look at all patients with AS who underwent spinal surgery and who had a diagnosis of epidural hematoma. Additionally, a systemic literature review was performed of all papers evaluating the incidence of epidural hematoma in patients with spine fractures. A single-institution, retrospective review of records from 2009 to 2020 yielded a total of 164 patients with AS who underwent spine surgery. Of those patients, 17 (10.4%) had epidural hematomas on imaging, with the majority requiring surgical decompression. These spine fractures occurred close to the cervicothoracic or thoracolumbar junction. The patients ranged in age from 51 to 88 years, and there were 14 males and 3 females in the cohort. Eight patients were administered an antiplatelet and/or anticoagulant agent, and the rest were not. All patients required surgical stabilization, with 64.7% of patients also requiring decompressive laminectomies for evacuation of the hematoma and spinal cord decompression. Only 1 death was reported in the series. There was a tendency toward neurological improvement after surgical intervention. AS has been a well-described pathologic process that leads to an increased risk of three-column injury in spine fracture, with an increased incidence of symptomatic epidural hematoma compared with patients without AS. Early recognition of this entity is important to ensure that appropriate surgical management includes addressing compression of the neural elements in addition to surgical stabilization.

An updated, computable MEDication-Indication resource for biomedical research

The MEDication-Indication (MEDI) knowledgebase has been utilized in research with electronic health records (EHRs) since its publication in 2013. To account for new drugs and terminology updates, we rebuilt MEDI to overhaul the knowledgebase for modern EHRs. Indications for prescribable medications were extracted using natural language processing and ontology relationships from six publicly available resources: RxNorm, Side Effect Resource 4.1, Mayo Clinic, WebMD, MedlinePlus, and Wikipedia. We compared the estimated precision and recall between the previous MEDI (MEDI-1) and the updated version (MEDI-2) with manual review. MEDI-2 contains 3031 medications and 186,064 indications. The MEDI-2 high precision subset (HPS) includes indications found within RxNorm or at least three other resources. MEDI-2 and MEDI-2 HPS contain 13% more medications and over triple the indications compared to MEDI-1 and MEDI-1 HPS, respectively. Manual review showed MEDI-2 achieves the same precision (0.60) with better recall (0.89 vs. 0.79) compared to MEDI-1. Likewise, MEDI-2 HPS had the same precision (0.92) and improved recall (0.65 vs. 0.55) than MEDI-1 HPS. The combination of MEDI-1 and MEDI-2 achieved a recall of 0.95. In updating MEDI, we present a more comprehensive medication-indication knowledgebase that can continue to facilitate applications and research with EHRs.

Contribution of Free-Text Comments to the Burden of Documentation: Assessment and Analysis of Vital Sign Comments in Flowsheets.

BackgroundDocumentation burden is a common problem with modern electronic health record (EHR) systems. To reduce this burden, various recording methods (eg, voice recorders or motion sensors) have been proposed. However, these solutions are in an early prototype phase and are unlikely to transition into practice in the near future. A more pragmatic alternative is to directly modify the implementation of the existing functionalities of an EHR system.ObjectiveThis study aims to assess the nature of free-text comments entered into EHR flowsheets that supplement quantitative vital sign values and examine opportunities to simplify functionality and reduce documentation burden.MethodsWe evaluated 209,055 vital sign comments in flowsheets that were generated in the Epic EHR system at the Vanderbilt University Medical Center in 2018. We applied topic modeling, as well as the natural language processing Clinical Language Annotation, Modeling, and Processing software system, to extract generally discussed topics and detailed medical terms (expressed as probability distribution) to investigate the stories communicated in these comments.ResultsOur analysis showed that 63.33% (6053/9557) of the users who entered vital signs made at least one free-text comment in vital sign flowsheet entries. The user roles that were most likely to compose comments were registered nurse, technician, and licensed nurse. The most frequently identified topics were the notification of a result to health care providers (0.347), the context of a measurement (0.307), and an inability to obtain a vital sign (0.224). There were 4187 unique medical terms that were extracted from 46,029 (0.220) comments, including many symptom-related terms such as “pain,” “upset,” “dizziness,” “coughing,” “anxiety,” “distress,” and “fever” and drug-related terms such as “tylenol,” “anesthesia,” “cannula,” “oxygen,” “motrin,” “rituxan,” and “labetalol.”ConclusionsConsidering that flowsheet comments are generally not displayed or automatically pulled into any clinical notes, our findings suggest that the flowsheet comment functionality can be simplified (eg, via structured response fields instead of a text input dialog) to reduce health care provider effort. Moreover, rich and clinically important medical terms such as medications and symptoms should be explicitly recorded in clinical notes for better visibility.

Electronic health records and clinician burnout: A story of three eras.

The study sought to provide physicians, informaticians, and institutional policymakers with an introductory tutorial about the history of medical documentation, sources of clinician burnout, and opportunities to improve electronic health records (EHRs). We now have unprecedented opportunities in health care, with the promise of new cures, improved equity, greater sensitivity to social and behavioral determinants of health, and data-driven precision medicine all on the horizon. EHRs have succeeded in making many aspects of care safer and more reliable. Unfortunately, current limitations in EHR usability and problems with clinician burnout distract from these successes. A complex interplay of technology, policy, and healthcare delivery has contributed to our current frustrations with EHRs. Fortunately, there are opportunities to improve the EHR and health system. A stronger emphasis on improving the clinician's experience through close collaboration by informaticians, clinicians, and vendors can combine with specific policy changes to address the causes of burnout. This tutorial is intended for clinicians, informaticians, policymakers, and regulators, who are essential participants in discussions focused on improving clinician burnout. Learners in biomedicine, regardless of clinical discipline, also may benefit from this primer and review. We include (1) an overview of medical documentation from a historical perspective; (2) a summary of the forces converging over the past 20 years to develop and disseminate the modern EHR; and (3) future opportunities to improve EHR structure, function, user base, and time required to collect and extract information.

Biomedical Data Science and Informatics Challenges to Implementing Pharmacogenomics with Electronic Health Records

Pharmacogenomic information must be incorporated into electronic health records (EHRs) with clinical decision support in order to fully realize its potential to improve drug therapy. Supported by various clinical knowledge resources, pharmacogenomic workflows have been implemented in several healthcare systems. Little standardization exists across these efforts, however, which limits scalability both within and across clinical sites. Limitations in information standards, knowledge management, and the capabilities of modern EHRs remain challenges for the widespread use of pharmacogenomics in the clinic, but ongoing efforts are addressing these challenges. Although much work remains to use pharmacogenomic information more effectively within clinical systems, the experiences of pioneering sites and lessons learned from those programs may be instructive for other clinical areas beyond genomics. We present a vision of what can be achieved as informatics and data science converge to enable further adoption of pharmacogenomics in the clinic.

Electronic health records systems and hospital clinical performance: a study of nationwide hospital data.

Electronic health records (EHRs) were expected to yield numerous benefits. However, early studies found mixed evidence of this. We sought to determine whether widespread adoption of modern EHRs in the US has improved clinical care. We studied hospitals reporting performance measures from 2008-2015 in the Centers for Medicare and Medicaid Services Hospital Compare database that also reported having an EHR in the American Hospital Association 2015 IT supplement. Using interrupted time-series analysis, we examined the association of EHR implementation, EHR vendor, and Meaningful Use status with 11 process measures and 30-day hospital readmission and mortality rates for heart failure, pneumonia, and acute myocardial infarction. A total of 1246 hospitals contributed 8222 hospital-years. Compared to hospitals without EHRs, hospitals with EHRs had significant improvements over time on 5 of 11 process measures. There were no substantial differences in readmission or mortality rates. Hospitals with CPSI EHR systems performed worse on several process and outcome measures. Otherwise, we found no substantial improvements in process measures or condition-specific outcomes by duration of EHR use, EHR vendor, or a hospital's Meaningful Use Stage 1 or Stage 2 status. In this national study of hospitals with modern EHRs, EHR use was associated with better process of care measure performance but did not improve condition-specific readmission or mortality rates regardless of duration of EHR use, vendor choice, or Meaningful Use status. Further research is required to understand why EHRs have yet to improve standard outcome measures and how to better realize the potential benefits of EHR systems.

Experiences of Transforming a Complex Nephrologic Care and Research Database into i2b2 Using the IDRT Tools.

The secondary use of data from electronic medical records has become an important factor to determine and to identify various causes of disease. For this reason, applications like informatics for integrating biology and the bedside (i2b2) offer a GUI-based front end to select patient cohorts. To make use of those tools, however, clinical data need to be extracted from the Electronic Health Record (EHR) system and integrated into the data schema of i2b2. We used TBase, a documentation system for nephrologic transplantations, as a source system and applied the Integrated Data Repository Toolkit (IDRT) for the Extract, Transform, and Load (ETL) process to load the data into i2b2. Since i2b2 uses an entity-attribute-value (EAV) schema, which is a fundamentally different way of modeling data in comparison to a standard relational schema in TBase, we evaluated if (a) the data relationship of the source system entities can still be represented in the i2b2 schema and if (b) the IDRT is a suitable solution for loading the data of a comprehensive data schema like TBase into i2b2. For that reason, we identified entities in the TBase data schema which were relevant for answering questions on cohort identification. By doing so, we found out that the entities had different structures that needed to be handled differently for the ETL process. Furthermore, the use of IDRT revealed shortcomings with regard to large input data and specific data structures that are part of most modern EHR systems. However, this project also showed that our way of modeling the TBase data in i2b2 has been proven to be successful in terms of answering the most common questions of clinicians on cohort identification.

Agile Acceptance Test-Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software.

BackgroundMoving to electronic health records (EHRs) confers substantial benefits but risks unintended consequences. Modern EHRs consist of complex software code with extensive local configurability options, which can introduce defects. Defects in clinical decision support (CDS) tools are surprisingly common. Feasible approaches to prevent and detect defects in EHR configuration, including CDS tools, are needed. In complex software systems, use of test–driven development and automated regression testing promotes reliability. Test–driven development encourages modular, testable design and expanding regression test coverage. Automated regression test suites improve software quality, providing a “safety net” for future software modifications. Each automated acceptance test serves multiple purposes, as requirements (prior to build), acceptance testing (on completion of build), regression testing (once live), and “living” design documentation. Rapid-cycle development or “agile” methods are being successfully applied to CDS development. The agile practice of automated test–driven development is not widely adopted, perhaps because most EHR software code is vendor-developed. However, key CDS advisory configuration design decisions and rules stored in the EHR may prove amenable to automated testing as “executable requirements.”ObjectiveWe aimed to establish feasibility of acceptance test–driven development of clinical decision support advisories in a commonly used EHR, using an open source automated acceptance testing framework (FitNesse).MethodsAcceptance tests were initially constructed as spreadsheet tables to facilitate clinical review. Each table specified one aspect of the CDS advisory’s expected behavior. Table contents were then imported into a test suite in FitNesse, which queried the EHR database to automate testing. Tests and corresponding CDS configuration were migrated together from the development environment to production, with tests becoming part of the production regression test suite.ResultsWe used test–driven development to construct a new CDS tool advising Emergency Department nurses to perform a swallowing assessment prior to administering oral medication to a patient with suspected stroke. Test tables specified desired behavior for (1) applicable clinical settings, (2) triggering action, (3) rule logic, (4) user interface, and (5) system actions in response to user input. Automated test suite results for the “executable requirements” are shown prior to building the CDS alert, during build, and after successful build.ConclusionsAutomated acceptance test–driven development and continuous regression testing of CDS configuration in a commercial EHR proves feasible with open source software. Automated test–driven development offers one potential contribution to achieving high-reliability EHR configuration. Vetting acceptance tests with clinicians elicits their input on crucial configuration details early during initial CDS design and iteratively during rapid-cycle optimization.

Acute Kidney Injury and Big Data.

The recognition of a standardized, consensus definition for acute kidney injury (AKI) has been an important milestone in critical care nephrology, which has facilitated innovation in prevention, quality of care, and outcomes research among the growing population of hospitalized patients susceptible to AKI. Concomitantly, there have been substantial advances in "big data" technologies in medicine, including electronic health records (EHR), data registries and repositories, and data management and analytic methodologies. EHRs are increasingly being adopted, clinical informatics is constantly being refined, and the field of EHR-enabled care improvement and research has grown exponentially. While these fields have matured independently, integrating the two has the potential to redefine and integrate AKI-related care and research. AKI is an ideal condition to exploit big data health care innovation for several reasons: AKI is common, increasingly encountered in hospitalized settings, imposes meaningful risk for adverse events and poor outcomes, has incremental cost implications, and has been plagued by suboptimal quality of care. In this concise review, we discuss the potential applications of big data technologies, particularly modern EHR platforms and health data repositories, to transform our capacity for AKI prediction, detection, and care quality.

The influence of system quality characteristics on health care providers' performance: Empirical evidence from Malaysia.

The Ministry of Health Malaysia initiated the total hospital information system (THIS) as the first national electronic health record system for use in selected public hospitals across the country. Since its implementation 15 years ago, there has been the critical requirement for a systematic evaluation to assess its effectiveness in coping with the current system, task complexity, and rapid technological changes. The study aims to assess system quality factors to predict the performance of electronic health in a single public hospital in Malaysia. Non-probability sampling was employed for data collection among selected providers in a single hospital for two months. Data cleaning and bias checking were performed before final analysis in partial least squares-structural equation modeling. Convergent and discriminant validity assessments were satisfied the required criterions in the reflective measurement model. The structural model output revealed that the proposed adequate infrastructure, system interoperability, security control, and system compatibility were the significant predictors, where system compatibility became the most critical characteristic to influence an individual health care provider's performance. The previous DeLone and McLean information system success models should be extended to incorporate these technological factors in the medical system research domain to examine the effectiveness of modern electronic health record systems. In this study, care providers' performance was expected when the system usage fits with patients' needs that eventually increased their productivity.

Clinicians and The Modern EHR: Statistician, Scribe or Storyteller?

The Health Information Technology for Economic and Clinical Health (HITECH) Act spearheaded the rapid adoption of electronic health records (EHR) in the United States. Although HITECH's mission is to implement data storage, order management, evidence-based decision support, electronic communication and connectivity, and administrative processes and reporting (i.e. billing and coding), the main purpose of the EHR for the practicing clinicians should remain the same -- to convey the story, facts, and plan for the patient’s health care to the reader. We define three categories of EHR notes: the statistical, the scribe, and the storyteller styles, the pros and cons of each, and how the strengths of each may be used for more effective written communication.

Changes to Stage 1 Meaningful Use in 2014: Impact on Radiologists

The goal of this work is to provide radiologists an update regarding changes to stage 1 of meaningful use in 2014. These changes were promulgated in the final rulemaking released by the Centers for Medicare and Medicaid Services and the Office of the National Coordinator for Health Information Technology in September 2012. Under the new rules, radiologists are exempt from meaningful use penalties provided that they are listed as radiologists under the Provider Enrollment, Chain and Ownership System (PECOS). A major caveat is that this exemption can be removed at any time. Additional concerns are discussed in the main text. Additional changes discussed include software editions independent of meaningful use stage (i.e., 2011 edition versus 2014 edition), changes to the definition of certified electronic health record technology (CEHRT), and changes to specific measures and exemptions to those measures. The new changes regarding stage 1 add complexity to an already complex program, but overall make achieving meaningful use a win-win situation for radiologists. There are no penalties for failure and incentive payments for success. The cost of upgrading to CEHRT may be much less than the incentive payments, adding a potential new source of revenue. Additional benefits may be realized if the radiology department can build upon a modern electronic health record to improve their practice and billing patterns. Meaningful use and electronic health records represent an important evolutionary step in US healthcare, and it is imperative that radiologists are active participants in the process.

Analysis of STAT Laboratory Turnaround Times Before and After Conversion of the Hospital Information System

Modern electronic health record systems (EHRS) reportedly offer advantages including improved quality, error prevention, cost reduction, and increased efficiency. This project reviewed the impact on specimen turnaround times (TAT) and percent compliance for specimens processed in a STAT laboratory after implementation of an upgraded EHRS. Before EHRS implementation, laboratory personnel received instruction and training for specimen processing. One laboratory member per shift received additional training. TAT and percent compliance data sampling occurred 4 times monthly for 13 months post-conversion and were compared with the mean of data collected for 3 months pre-conversion. Percent compliance was gauged using a benchmark of reporting 95% of all specimens within 7 min from receipt. Control charts were constructed for TAT and percent compliance with control limits set at 2 SD and applied continuously through the data collection period. TAT recovered to pre-conversion levels by the 6th month post-conversion. Percent compliance consistently returned to pre-conversion levels by the 10th month post-conversion. Statistical analyses revealed the TAT were significantly longer for 3 months post-conversion (P < .001) compared with pre-conversion levels. Statistical significance was not observed for subsequent groups. Percent compliance results were significantly lower for 6 months post-conversion (P < .001). Statistical significance was not observed for subsequent groups. Extensive efforts were made to train and prepare personnel for challenges expected after the EHRS upgrade. Specific causes identified with the upgraded EHRS included multiple issues involving personnel and the EHRS. These data suggest that system and user issues contributed to delays in returning to pre-conversion TAT and percent compliance levels following the upgrade in the EHRS.

Increasing Compliance With Documentation Using Modern Electronic Health Record Systems Toward Quality Improvement in Radiation Practice Medical Coding

Increasing Compliance With Documentation Using Modern Electronic Health Record Systems Toward Quality Improvement in Radiation Practice Medical Coding