Quality of EHR data extractions for studies of preterm birth in a tertiary care center: guidelines for obtaining reliable data.

BACKGROUND: The use of Electronic Health Records (EHR) has increased significantly in the past 15 years. This study compares electronic vs. manual data abstractions from an EHR for accuracy. While the dataset is limited to preterm birth data, our work is generally applicable. We enumerate challenges to reliable extraction, and state guidelines to maximize reliability. METHODS: An Epic™ EHR data extraction of structured data values from 1,772 neonatal records born between the years 2001-2011 was performed. The data were directly compared to a manually-abstracted database. Specific data values important to studies of perinatology were chosen to compare discrepancies between the two databases. RESULTS: Discrepancy rates between the EHR extraction and the manual database were calculated for gestational age in weeks (2.6 %), birthweight (9.7 %), first white blood cell count (3.2 %), initial hemoglobin (11.9 %), peak total and direct bilirubin (11.4 % and 4.9 %), and patent ductus arteriosus (PDA) diagnosis (12.8 %). Using the discrepancies, errors were quantified in both datasets using chart review. The EHR extraction errors were significantly fewer than manual abstraction errors for PDA and laboratory values excluding neonates transferred from outside hospitals, but significantly greater for birth weight. Reasons for the observed errors are discussed. CONCLUSIONS: We show that an EHR not modified specifically for research purposes had discrepancy ranges comparable to a manually created database. We offer guidelines to minimize EHR extraction errors in future study designs. As EHRs become more research-friendly, electronic chart extractions should be more efficient and have lower error rates compared to manual abstractions.

Balanced Business Decision-making for Clinical Practices: Fulfilling "Mission" and "Margin".

Clinical practices, both private and academic, need to be profitable to sustain themselves and grow. To manage them and drive profits, one needs leadership that has a well-rounded understanding of multiple facets. When business decisions are based on financial data alone, they meet the goal of profit margin but alienate clinicians; when made using clinical data alone, they fulfill the clinical mission but ignore the bottom line. Here, we explain the benefits of a sound business-minded leadership that integrates the nuances of financial data, the cadence of clinical practices, and the value of resources, and makes meaningful business decisions. These decisions create a strong bond between physicians and their administrative leaders, aligning their "mission" (provide better care), and their "margin" (profitability and growth). We explain critical aspects of each source of information and how to use them together to make business decisions. Recognizing that clinicians may not have access to methods of financial analysis, we also supply a prepopulated Excel spreadsheet that has all equations baked in, so that it can be readily used, filling in their own data to generate financial ratios. This work explains how sound decisions can be made using financial metrics, clinical data (here, plastic surgery), and resource utilization, to identify areas that can be improved and take steps to achieve results.

Budgets: How They Are Planned, Prepared, and Managed.

Background: Budget planning and execution is as difficult as it is vital to any practice, whether academic, private, or group. Well-planned and executed budgets are a source of revenue and growth that fuels the practice for the next cycle. Conversely, poorly planned budget is disastrous, and a badly executed one invariably leads to unrecoverable losses. Many clinicians, especially those in academic centers, are not involved in budget-planning preparation and yet are held accountable for their yearly performance in relation to the budget. Methods: Key processes for budget planning and their significance are identified. Integrating these steps with the needs of a clinical practice, a stepwise method is described for both clinicians and administrators to work together to plan, prepare, and manage budgets. Results: Relevant examples of how budgets affect clinical workflow and common pitfalls of budget planning and mitigation methods are identified. A simplified systematic approach allows for a streamlined, smooth budget-planning process that involves faculty and staff, which holds them accountable for the year-long performance of the entire clinical team. Conclusions: A systematic proactive approach to budget-planning, preparation, and management provides a financial direction to the department; tracks performance; allows growth; and provides the flexibility to stay on track, change course, or reassign resources.

The Importance of Financial Statements in Clinical Practice.

Background: Financial statements provide vital information to department chiefs and hospital leadership alike. They reflect departmental performance and guide critical financial decisions for their teams. However, financial statements can be inherently difficult to read and interpret and require time and attention, understandably challenging for busy clinicians. Methods: Here, we aimed to demystify the several types of financial statements, including profit and loss statements, balance sheets, and cash flow statements, and explain what they reveal (and ignore). We describe key performance indicators based on these statements that are routinely used by hospital administrations. This work targets clinicians, team leaders, academic faculty, and administrators alike, recognizing that all of them share the same goals. Results: Mastering the basics of financial statements and using the information within them creates a healthier clinical practice. In turn, it enhances provider satisfaction and enables the team to deliver patient care without financial anxiety. Conclusions: Understanding financial statements helps shared decision-making between clinicians and their administrators-strengthening partnerships that synergistically drive revenue, profitability, and growth.

Revenue Cycle Management: The Art and the Science.

Simply working hard is not enough to maintain a profitable clinical practice. Prompt and complete payment for services is just as critical. Revenue cycle management (RCM) tracks the payment process from patient scheduling through treatment, coding, billing, and reimbursement. Even though reimbursement rates for service codes are preset, and the service is documented, this apparently straightforward process is complicated by insurance payors, negotiated contracts, coding requirements, compliance regulators, and an ever-changing reimbursement environment. Not typically trained in RCM, physicians struggle with its demands of timeliness, accuracy, paperwork, and the constant scrutiny for underpayment or unfulfilled reimbursements. Consequently, they often relent to the pressures and simply accept the decreased reimbursements as "cost of doing business" or else relegate RCM to others on the team. In either case, they leave significant amounts of money on the table. Using published work in health care and other allied sectors, we present a systematic method to understand and improve RCM processes. It also creates a strong partnership between clinicians and their administrative counterparts. Optimizing RCM improves patient experience, reduces the time between submission of claims and payment, eliminates fraud at both the coding and patient levels, and increases cash flow, all of which create a financially stable clinical practice.

Radiation exposure and breast cancer: lessons from Chernobyl.

The lessons learned from the Chernobyl disaster have become increasingly important after the second anniversary of the Fukushima, Japan nuclear accident. Historically, data from the Chernobyl reactor accident 27 years ago demonstrated a strong correlation with thyroid cancer, but data on the radiation effects of Chernobyl on breast cancer incidence have remained inconclusive. We reviewed the published literature on the effects of the Chernobyl disaster on breast cancer incidence, using Medline and Scopus from the time of the accident to December of 2010. Our findings indicate limited data and statistical flaws. Other confounding factors, such as discrepancies in data collection, make interpretation of the results from the published literature difficult. Re-analyzing the data reveals that the incidence of breast cancer in Chernobyl-disaster-exposed women could be higher than previously thought. We have learned little of the consequences of radiation exposure at Chernobyl except for its effects on thyroid cancer incidence. Marking the 27th year after the Chernobyl event, this report sheds light on a specific, crucial and understudied aspect of the results of radiation from a gruesome nuclear power plant disaster.

Testing the Decision Support Tool for Responsible Pain Management for Headache and Facial Pain Diagnosis with Opioid-Risk-Stratified Treatment.

In primary and urgent care, headache and facial pain are common and challenging to diagnose and manage, especially with using opioids appropriately. We therefore developed the Decision Support Tool for Responsible Pain Management (DS-RPM) to assist healthcare providers in diagnosis (including multiple simultaneous diagnoses), workup (including triage), and opioid-risk-informed treatment. A primary goal was to supply sufficient explanations of DS-RPM's functions allowing critique. We describe the process of iteratively designing DS-RPM adding clinical content and testing/defect discovery. We tested DS-RPM remotely with 21 clinician-participants using three vignettes-cluster headache, migraine, and temporal arteritis-after first training to use DS-RPM with a trigeminal-neuralgia vignette. Their evaluation was both quantitative (usability/acceptability) and qualitative using semi-structured interviews. The quantitative evaluation used 12 Likert-type questions on a 1-5 scale, where 5 represented the highest rating. The mean ratings ranged from 4.48 to 4.95 (SDs ranging 0.22-1.03). Participants initially found structured data entry intimidating but adapted and appreciated its comprehensiveness and speed of data capture. They perceived DS-RPM as useful for teaching and clinical practice, making several enhancement suggestions. The DS-RPM was designed, created, and tested to facilitate best practice in management of patients with headaches and facial pain. Testing the DS-RPM with vignettes showed strong functionality and high usability/acceptability ratings from healthcare providers. Risk stratifying for opioid use disorder to develop a treatment plan for headache and facial pain is possible using vignettes. During testing, we considered the need to adapt usability/acceptability evaluation tools for clinical decision support, and future directions.

Requirements for guidelines systems: implementation challenges and lessons from existing software-engineering efforts.

BACKGROUND: A large body of work in the clinical guidelines field has identified requirements for guideline systems, but there are formidable challenges in translating such requirements into production-quality systems that can be used in routine patient care. Detailed analysis of requirements from an implementation perspective can be useful in helping define sub-requirements to the point where they are implementable. Further, additional requirements emerge as a result of such analysis. During such an analysis, study of examples of existing, software-engineering efforts in non-biomedical fields can provide useful signposts to the implementer of a clinical guideline system. METHODS: In addition to requirements described by guideline-system authors, comparative reviews of such systems, and publications discussing information needs for guideline systems and clinical decision support systems in general, we have incorporated additional requirements related to production-system robustness and functionality from publications in the business workflow domain, in addition to drawing on our own experience in the development of the Proteus guideline system (http://proteme.org). RESULTS: The sub-requirements are discussed by conveniently grouping them into the categories used by the review of Isern and Moreno 2008. We cite previous work under each category and then provide sub-requirements under each category, and provide example of similar work in software-engineering efforts that have addressed a similar problem in a non-biomedical context. CONCLUSIONS: When analyzing requirements from the implementation viewpoint, knowledge of successes and failures in related software-engineering efforts can guide implementers in the choice of effective design and development strategies.

Tuberculosis conundrum - current and future scenarios: A proposed comprehensive approach combining laboratory, imaging, and computing advances.

Tuberculosis (TB) remains a global threat, with the rise of multiple and extensively drug resistant TB posing additional challenges. The International health community has set various 5-yearly targets for TB elimination: mathematical modelling suggests that a 2050 target is feasible with a strategy combining better diagnostics, drugs, and vaccines to detect and treat both latent and active infection. The availability of rapid and highly sensitive diagnostic tools (Gene-Xpert, TB-Quick) will vastly facilitate population-level identification of TB (including rifampicin resistance and through it, multi-drug-resistant TB). Basic-research advances have illuminated molecular mechanisms in TB, including the protective role of Vitamin D. Also, Mycobacterium tuberculosis impairs the host immune response through epigenetic mechanisms (histone-binding modulation). Imaging will continue to be key, both for initial diagnosis and follow-up. We discuss advances in multiple imaging modalities to evaluate TB tissue changes, such as molecular imaging techniques (including pathogen-specific positron emission tomography imaging agents), non-invasive temporal monitoring, and computing enhancements to improve data acquisition and reduce scan times. Big data analysis and Artificial Intelligence (AI) algorithms, notably in the AI sub-field called "Deep Learning", can potentially increase the speed and accuracy of diagnosis. Additionally, Federated learning makes multi-institutional/multi-city AI-based collaborations possible without sharing identifiable patient data. More powerful hardware designs - e.g., Edge and Quantum Computing- will facilitate the role of computing applications in TB. However, "Artificial Intelligence needs real Intelligence to guide it!" To have maximal impact, AI must use a holistic approach that incorporates time tested human wisdom gained over decades from the full gamut of TB, i.e., key imaging and clinical parameters, including prognostic indicators, plus bacterial and epidemiologic data. We propose a similar holistic approach at the level of national/international policy formulation and implementation, to enable effective culmination of TB's endgame, summarizing it with the acronym "TB - REVISITED".

Augmentation of literature review of COVID-19 radiology.

We suggest an augmentation of the excellent comprehensive review article titled "Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the coronavirus disease 2019 (COVID-19) pandemic" under the following categories: (1) "Inclusion of additional radiological features, related to pulmonary infarcts and to COVID-19 pneumonia"; (2) "Amplified discussion of cardiovascular COVID-19 manifestations and the role of cardiac magnetic resonance imaging in monitoring and prognosis"; (3) "Imaging findings related to fluorodeoxyglucose positron emission tomography, optical, thermal and other imaging modalities/devices, including 'intelligent edge' and other remote monitoring devices"; (4) "Artificial intelligence in COVID-19 imaging"; (5) "Additional annotations to the radiological images in the manuscript to illustrate the additional signs discussed"; and (6) "A minor correction to a passage on pulmonary destruction".

Web-browser encryption of personal health information.

BACKGROUND: Electronic health records provide access to an unprecedented amount of clinical data for research that can accelerate the development of effective medical practices. However it is important to protect patient confidentiality, as many medical conditions are stigmatized and disclosure could result in personal and/or financial loss. RESULTS: We describe a system for remote data entry that allows the data that would identify the patient to be encrypted in the web browser of the person entering the data. These data cannot be decrypted on the server by the staff at the data center but can be decrypted by the person entering the data or their delegate. We developed this system to solve a problem that arose in the context of clinical research, but it is applicable in a range of situations where sensitive information is stored and updated in a database and it is necessary to ensure that it cannot be viewed by any except those intentionally given access. CONCLUSION: By developing this system, we are able to centralize the collection of some patient data while minimizing the risk that protected health information be made available to study personnel who are not authorized to use it.

Safe clinical photography: best practice guidelines for risk management and mitigation.

Clinical photography is an essential component of patient care in plastic surgery. The use of unsecured smartphone cameras, digital cameras, social media, instant messaging, and commercially available cloud-based storage devices threatens patients' data safety. This paper Identifies potential risks of clinical photography and heightens awareness of safe clinical photography. Specifically, we evaluated existing risk-mitigation strategies globally, comparing them to industry standards in similar settings, and formulated a framework for developing a risk-mitigation plan for avoiding data breaches by identifying the safest methods of picture taking, transfer to storage, retrieval, and use, both within and outside the organization. Since threats evolve constantly, the framework must evolve too. Based on a literature search of both PubMed and the web (via Google) with key phrases and child terms (for PubMed), the risks and consequences of data breaches in individual processes in clinical photography are identified. Current clinical-photography practices are described. Lastly, we evaluate current risk mitigation strategies for clinical photography by examining guidelines from professional organizations, governmental agencies, and non-healthcare industries. Combining lessons learned from the steps above into a comprehensive framework that could contribute to national/international guidelines on safe clinical photography, we provide recommendations for best practice guidelines. It is imperative that best practice guidelines for the simple, safe, and secure capture, transfer, storage, and retrieval of clinical photographs be co-developed through cooperative efforts between providers, hospital administrators, clinical informaticians, IT governance structures, and national professional organizations. This would significantly safeguard patient data security and provide the privacy that patients deserve and expect.

Overcoming the Patent Gap: A Guide to Patenting for Plastic Surgeons.

BACKGROUND: Patenting protects innovation, fosters academic incentives, promotes competition, and generates new revenue for clinician-inventors and their institutions. Despite these benefits, and despite plastic surgery's history of innovation, plastic surgery-related patent applications are few. The goal of this article was to use unpublished data and formulate a robust discussion. METHODS: The U.S. Patent and Trademark Office's boolean search was investigated between the timeline of 1975 and June 23, 2020, to identify patents related to the key phrases to contrast patent (both, issued and filed) tally in each specialty. Queries for two key phrases related to plastic surgery and a core plastic surgical activity, both with and without the added term "plastic surgery," were performed. RESULTS: Total patents with "cardiology" outnumber those with "plastic surgery" by 22,450 versus 7749 (i.e., almost 3:1). The overwhelming number of patents with "cosmetic" are non-plastic-surgery related: 87,910 total versus 2782 for those with plastic surgery. The corresponding numbers for "wound healing" are 36,359 versus 2703. Reasons for the patent gap between clinical innovations in plastic surgery and number of patents in our field are identified. Clear steps to bridge this gap are delineated that include a step-by-step process for patenting, from idea creation through commercialization. The authors propose "breakthrough to bank," a framework wherein academic medical centers can create an environment of innovative freedom, establish the infrastructure for technological transfer of intellectual property, and generate a pipeline toward commercial applications. CONCLUSIONS: Innovation and inventions are important hallmarks for the progress of plastic surgery. Using a stepwise process, it may be possible to convert ideas into patents.

Determining correspondences between high-frequency MedDRA concepts and SNOMED: a case study.

BACKGROUND: The Systematic Nomenclature of Medicine Clinical Terms (SNOMED CT) is being advocated as the foundation for encoding clinical documentation. While the electronic medical record is likely to play a critical role in pharmacovigilance - the detection of adverse events due to medications - classification and reporting of Adverse Events is currently based on the Medical Dictionary of Regulatory Activities (MedDRA). Complete and high-quality MedDRA-to-SNOMED CT mappings can therefore facilitate pharmacovigilance. The existing mappings, as determined through the Unified Medical Language System (UMLS), are partial, and record only one-to-one correspondences even though SNOMED CT can be used compositionally. Efforts to map previously unmapped MedDRA concepts would be most productive if focused on concepts that occur frequently in actual adverse event data. We aimed to identify aspects of MedDRA that complicate mapping to SNOMED CT, determine pattern in unmapped high-frequency MedDRA concepts, and to identify types of integration errors in the mapping of MedDRA to UMLS. METHODS: Using one years' data from the US Federal Drug Administrations Adverse Event Reporting System, we identified MedDRA preferred terms that collectively accounted for 95% of both Adverse Events and Therapeutic Indications records. After eliminating those already mapping to SNOMED CT, we attempted to map the remaining 645 Adverse-Event and 141 Therapeutic-Indications preferred terms with software assistance. RESULTS: All but 46 Adverse-Event and 7 Therapeutic-Indications preferred terms could be composed using SNOMED CT concepts: none of these required more than 3 SNOMED CT concepts to compose. We describe the common composition patterns in the paper. About 30% of both Adverse-Event and Therapeutic-Indications Preferred Terms corresponded to single SNOMED CT concepts: the correspondence was detectable by human inspection but had been missed during the integration process, which had created duplicated concepts in UMLS. CONCLUSIONS: Identification of composite mapping patterns, and the types of errors that occur in the MedDRA content within UMLS, can focus larger-scale efforts on improving the quality of such mappings, which may assist in the creation of an adverse-events ontology.

Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview.

Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We also identify recent research findings and practice changes. The recent advances in machine learning and natural language processing (NLP) are a prominent development in health informatics overall and relevant in emergency medicine (EM). A basic comprehension of machine-learning algorithms is the key to understand the recent usage of artificial intelligence in healthcare. We are using NLP more in clinical use for documentation. NLP has started to be used in research to identify clinically important diseases and conditions. Health informatics has the potential to benefit both healthcare providers and patients. We cover two powerful tools from health informatics for EM clinicians and researchers by describing the previous successes and challenges and conclude with their implications to emergency care.

Database architectures for neuroscience applications.

To determine effective database architecture for a specific neuroscience application, one must consider the distinguishing features of research databases and the requirements that the particular application must meet. Research databases manage diverse types of data, and their schemas evolve fairly steadily as domain knowledge advances. Database search and controlled-vocabulary access across the breadth of the data must be supported. We provide examples of design principles employed by our group as well as others that have proven successful and also introduce the appropriate use of entity-attribute-value (EAV) modeling. Most important, a robust architecture requires a significant metadata component, which serves to describe the individual types of data in terms of function and purpose. Recording validation constraints on individual items, as well as information on how they are to be presented, facilitates automatic or semi-automatic generation of robust user interfaces.

Interoperability across neuroscience databases.

Data interoperability between well-defined domains is currently performed by leveraging Web services. In the biosciences, more specifically in neuroscience, robust data interoperability is more difficult to achieve due to data heterogeneity, continuous domain changes, and the constant creation of new semantic data models (Nadkarni et al., J Am Med Inform Assoc 6, 478-93, 1999; Miller et al., J Am Med Inform Assoc 8, 34-48, 2001; Gardner et al., J Am Med Inform Assoc 8, 17-33, 2001). Data heterogeneity in neurosciences is primarily due to its multidisciplinary nature. This results in a compelling need to integrate all available neuroscience information to improve our understanding of the brain. Researchers associated with neuroscience initiatives such as the human brain project (HBP) (Koslow and Huerta, Neuroinformatics: An Overview of the Human Brain Project, 1997), the Bioinformatics Research Network (BIRN), and the Neuroinformatics Information Framework (NIF) are exploring mechanisms to allow robust interoperability between these continuously evolving neuroscience databases. To accomplish this goal, it is crucial to orchestrate technologies such as database mediators, metadata repositories, semantic metadata annotations, and ontological services. This chapter introduces the importance of database interoperability in neurosciences. We also describe current data sharing and integration mechanisms in genera. We conclude with data integration in bioscience and present approaches on neuroscience data sharing.

Guidelines for the effective use of entity-attribute-value modeling for biomedical databases.

PURPOSE: To introduce the goals of EAV database modeling, to describe the situations where entity-attribute-value (EAV) modeling is a useful alternative to conventional relational methods of database modeling, and to describe the fine points of implementation in production systems. METHODS: We analyze the following circumstances: (1) data are sparse and have a large number of applicable attributes, but only a small fraction will apply to a given entity; (2) numerous classes of data need to be represented, each class has a limited number of attributes, but the number of instances of each class is very small. We also consider situations calling for a mixed approach where both conventional and EAV design are used for appropriate data classes. RESULTS AND CONCLUSIONS: In robust production systems, EAV-modeled databases trade a modest data sub-schema for a complex metadata sub-schema. The need to design the metadata effectively makes EAV design potentially more challenging than conventional design.

Informatics tools to improve clinical research study implementation.

BACKGROUND: There are numerous potential sources of problems when performing complex clinical research trials. These issues are compounded when studies are multi-site and multiple personnel from different sites are responsible for varying actions from case report form design to primary data collection and data entry. METHODS: We describe an approach that emphasizes the use of a variety of informatics tools that can facilitate study coordination, training, data checks and early identification and correction of faulty procedures and data problems. The paper focuses on informatics tools that can help in case report form design, procedures and training and data management. CONCLUSION: Informatics tools can be used to facilitate study coordination and implementation of clinical research trials.