Data standards may be wonky, but they will transform health care

A Each of us should reasonably expect that health systems invest as much into providing clinicians with insights to make the right diagnosis or choose the right treatment as they currently invest in determining the right ad to display on a webpage. Although that hasn’t been the case so far, there’s now an opportunity to take a quantum leap to meet that expectation.story with enormous implications for the health of all Americans is likely flying below their radar and that of their physicians. In a nutshell, it’s this: A proposed rule that sets data standards will make electronic health information more accessible to patients and doctors through smartphone-style apps and will transform health care.

Most Americans are familiar with this scenario: During the “conversation” parts of a medical appointment, the doctor faces a computer screen and types information into an electronic medical record system. Such systems store data on hundreds of millions of Americans.

Yet even with all of this data entry going on, it is a struggle for patients to get copies of their records, and an even bigger one to get them in useful, digital formats. Even more alarming, despite the vast amount of data collected by electronic medical record systems, little of it is used to help clinicians make decisions about their patients’ care. That’s unacceptable.

Each of us should reasonably expect that health systems invest as much into providing clinicians with insights to make the right diagnosis or choose the right treatment as they currently invest in determining the right ad to display on a webpage. Although that hasn’t been the case so far, there’s now an opportunity to take a quantum leap to meet that expectation.

The full STAT article can be viewed at this link.  

Effect of Organization-Directed Workplace Interventions on Physician Burnout: A Systematic Review

To assess the impact of organization-directed workplace interventions on physician burnout, including stress or job satisfaction in all settings, we conducted a systematic review of the literature published from January 1, 2007, to October 3, 2018, from multiple databases. Manual searches of grey literature and bibliographies were also performed. Of the 633 identified citations, 50 met inclusion criteria. Four unique categories of organization-directed workplace interventions were identified. Teamwork involved initiatives to incorporate scribes or medical assistants into electronic health record (EHR) processes, expand team responsibilities, and improve communication among physicians. Time studies evaluated the impact of schedule adjustments, duty hour restrictions, and time-banking initiatives. Transitions referred to workflow changes such as process improvement initiatives or policy changes within the organization. Technology related to the implementation or improvement of EHRs. Of the 50 included studies, 35 (70.0%) reported interventions that successfully improved the 3 measures of physician burnout, job satisfaction, and/or stress. The largest benefits resulted from interventions that improved processes, promoted teambased care, and incorporated the use of scribes/medical assistants to complete EHR documentation and tasks. Implementation of EHR interventions to improve clinical workflows worsened burnout, but EHR improvements had positive effects. Time interventions had mixed effects on burnout. The results of our study suggest that organization-directed workplace interventions that improve processes, optimize EHRs, reduce clerical burden by the use of scribes, and implement team-based care can lessen physician burnout. Benefits of process changes can enhance physician resiliency, augment care provided by the team, and optimize the coordination and communication of patient care and health information.

The full article can be downloaded below.  

Implementing Real-Time Clinical Decision Support Applications on OpenICE: A Case Study Using the National Early Warning System Algorithm

This paper presents the design and implementation of a software application, called MEWS, that implements the Royal College of Physician’s National Early Warning (scoring) System on the OpenICE interoperable platform. The MEWS app, as a real-time clinical decision support (RT-CDS) application, does not require the use of an Electronic Health Record System to support its operation. Instead, it is able to receive patient vital sign measurements from any patient physiological monitoring device connected to OpenICE, irrespective of the device manufacturer. Based on the received vital signs, MEWS calculates an overall score indicating the monitored patient’s current status and is intended to direct clinicians to patients showing signs of deteriorating conditions and hence needing immediate intervention. The implementation and deployment of the MEWS app on OpenICE presents a preliminary step to understand the challenge of establishing (data) interface protocols to enable medical device interoperability generally, and for RT-CDS applications in particular, and to establish requirements for bridging the gap of current industrial standardization activities in addressing this challenge.

The full article can be downloaded below.  

An awakening in medicine: the partnership of humanity and intelligent machines

In concurrence with the introduction of the internet, widely networked computers, and the collection of large amounts of digital data, the medical profession as a whole has become more self-aware and self-critical. It is increasingly apparent that suboptimal decisions are made at times and, on other occasions, are fatally flawed. Most clinical decisions rest largely on what is referred to as the art of medicine: that is, decision-making that is based on inconsistent and incomplete provider knowledge; variable skills, training, and experience; and last but not the least, an array of biases. Unsurprisingly, the result is an unacceptable degree of care variation that is not explained by patient factors or the clinical context. Every minute, a medical decision is being made somewhere that could be more informed, more objective, more precise, and more safe. How does medicine move on to adapt to an era of big data and a need to make consistent, data driven, evidence and value-based clinical decisions?

The full article can be downloaded below.  

Physician Burnout: Are Too Many Patients Making Doctors Sick?

It’s common knowledge that doctors work long hours. From late nights studying in medical school to the 28-hour shifts many young doctors experience during residency, physicians are conditioned to push their bodies and minds to the extreme in pursuit of a noble goal – making patients better. What if, however, those very patients are adversely affecting their doctor’s well-being?

The U.S. is facing what has been called a severe and growing epidemic of physician burnout, with nearly half of all clinicians reporting feelings of exhaustion, depression, depersonalization and failure. The epidemic threatens to affect not only the health of physicians but that of patients as well, since tired and overworked doctors are inherently less engaged and more prone to mistakes. In fact, a recent Stanford study found that burnout influences quality of care, patient safety and patient satisfaction – and that medical errors double among physicians suffering from the syndrome. With over 1.1 million physicians in the U.S. and a rapidly growing pool of patients, physician burnout is everyone’s issue. 

How did we get here and what can be done to reduce the burden placed on physicians?  

The full Forbes article can be viewed at this link.  

AI needs patients’ voices in order to revolutionize health care

Patients’ stories — what doctors call patient histories — are the bedrock of medicine. “Listen to your patient; they are telling you the diagnosis,” an aphorism attributed to Dr. William Osler, the founder of modern medicine, still holds true today. The disappearance of patients’ stories from electronic health records could be one reason that artificial intelligence and machine learning have so far failed to deliver their promised revolution of health care.

The medical industry’s fascination with artificial intelligence is understandable. Advancements in medicine have dramatically improved patient outcomes, and there is every reason to believe that machine learning, deep learning, artificial intelligence, and the like will do the same. But before we jump on the AI bandwagon, I offer this caution: consider the source of the data it is dependent on.

The full STAT article can be viewed at this link.  

HealthGuard: A Machine Learning-Based Security Framework for Smart Healthcare Systems

The integration of Internet-of-Things and pervasive computing in medical devices have made the modern healthcare system ”smart.” Today, the function of the healthcare system is not limited to treat the patients only. With the help of implantable medical devices and wearables, Smart Healthcare System (SHS) can continuously monitor different vital signs of a patient and automatically detect and prevent critical medical conditions. However, these increasing functionalities of SHS raise several security concerns and attackers can exploit the SHS in numerous ways: they can impede normal function of the SHS, inject false data to change vital signs, and tamper a medical device to change the outcome of a medical emergency. In this paper, we propose HealthGuard, a novel machine learning-based security framework to detect malicious activities in a SHS. HealthGuard observes the vital signs of different connected devices of a SHS and correlates the vitals to understand the changes in body functions of the patient to distinguish benign and malicious activities. HealthGuard utilizes four different machine learningbased detection techniques (Artificial Neural Network, Decision Tree, Random Forest, k-Nearest Neighbor) to detect malicious activities in a SHS. We trained HealthGuard with data collected for eight different smart medical devices for twelve benign events including seven normal user activities and five diseaseaffected events. Furthermore, we evaluated the performance of HealthGuard against three different malicious threats. Our extensive evaluation shows that HealthGuard is an effective security framework for SHS with an accuracy of 91% and an F1 score of 90%. 

The full article can be downloaded below.  

U.S. Opioid Epidemic: Impact on Public Health and Review of Prescription Drug Monitoring Programs (PDMPs)

In recent years, the devastating effects of U.S. opioid epidemic has been making news headlines. This report explores background information and trends on opioid misuse, overdose fatalities and its impact on public health. In addition, various efforts to improve surveillance, timeliness of data and Prescription Drug Monitoring Program (PDMP) integration and interoperability are reviewed.

PubMed and internet searches were performed to find information on the U.S. opioid epidemic. In addition, searches were performed to retrieve information about PDMPs and state-specific mandates along with presentation slides and learnings from the 2018 National Rx Drug Abuse & Heroin Summit in Atlanta, GA.

It is clear that the U.S. opioid epidemic has a tremendous impact on public health including the next generation of children. Various data, surveillance & technology-driven efforts including CDCFunded Enhanced State Opioid Overdose Surveillance Program (ESOOS) and use of telemedicine for opioid use disorder treatment aim to improve prevention, treatment and targeted interventions. In addition, PDMP integration and interoperability efforts are advancing to provide prescribers meaningful decision support tools.

The opioid epidemic has a complex impact on public health intertwined with variable factors such as mental health and social determinants of health. Given the statistics and studies that suggest many of the illicit opioid users start with prescription opioids, continued advancement in the area of PDMP integration and interoperability is necessary. The PDMP integrated clinical decision support systems need to supply to healthcare providers access to complete, timely and evidence-based information that can meaningfully inform prescribing decisions and communication with patients that affect measurable outcomes.

While Prescription Drug Monitoring Programs (PDMPs) are valuable tools for providers in making informed prescribing decisions, the variable state mandates and varying degrees of integration and interoperability across states may limit their potential as meaningful decision support tools. Sharing best practices, challenges and lessons learned among states and organizations may inform strategic and systematic use of PDMPs to improve public health outcomes.

The full article can be downloaded below.  

As Medicine Evolves, So Too Must Those Who Assure Its Quality

The past few years have illustrated the startling speed with which medicine can evolve. Since 2018, the US Food & Drug Administration (FDA) has approved first-of-their-kind drugs based on RNA, gene therapy, and cancer-killing chimeric antigen receptor (CAR) T-cells and signed off on human trials to explore the clinical use of CRISPR-mediated genome editing. And throughout this process, the US Pharmacopeia (USP) has been working in the background to ensure that quality standards are in place for new medical products reaching the market. “200 years ago, our first monographs were basically recipes: ‘take bark from this tree and boil it for this long and you should get a brown liquid’,” says Michael Levy, Head of Research & Innovation (R&I) at USP. “Obviously we've evolved tremendously since then, but it’s just a continuation of what we’ve always done—we’re just doubling down on it.”

Regular revisions to quality standards to accommodate advances in knowledge and changes in medical practice were built into the USP process by its founders. Today, to ensure its standards stay current, USP also works to stay ahead of the rapidly changing technology curve. Well before a cutting-edge medicine reaches the market, the underlying tools and techniques are already percolating up into the scientific literature and presentations at international conferences. USP combs through this early-stage work and projects which prospects seem most likely to impact the quality of therapeutics within the next decade or so. “We ask what’s on the horizon, what are the quality issues potentially associated with that trend or technology, and how does USP need to respond,” says Levy.  USP then explores some of these technologies, working through a typical research approach with preliminary proof-of-concept work potentially followed by longer-term “incubation projects” conducted by subject-matter experts.

The full article from Scientific American can be viewed at this link.  

A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis

Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases using medical imaging.

In this systematic review and meta-analysis, we searched Ovid-MEDLINE, Embase, Science Citation Index, and Conference Proceedings Citation Index for studies published from Jan 1, 2012, to June 6, 2019. Studies comparing the diagnostic performance of deep learning models and health-care professionals based on medical imaging, for any disease, were included. We excluded studies that used medical waveform data graphics material or investigated the accuracy of image segmentation rather than disease classification. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. Studies undertaking an out-of-sample external validation were included in a meta-analysis, using a unified hierarchical model. This study is registered with PROSPERO, CRD42018091176.

Our search identified 31 587 studies, of which 82 (describing 147 patient cohorts) were included. 69 studies provided enough data to construct contingency tables, enabling calculation of test accuracy, with sensitivity ranging from 9·7% to 100·0% (mean 79·1%, SD 0·2) and specificity ranging from 38·9% to 100·0% (mean 88·3%, SD 0·1). An out-of-sample external validation was done in 25 studies, of which 14 made the comparison between deep learning models and health-care professionals in the same sample. Comparison of the performance between health-care professionals in these 14 studies, when restricting the analysis to the contingency table for each study reporting the highest accuracy, found a pooled sensitivity of 87·0% (95% CI 83·0–90·2) for deep learning models and 86·4% (79·9–91·0) for health-care professionals, and a pooled specificity of 92·5% (95% CI 85·1–96·4) for deep learning models and 90·5% (80·6–95·7) for health-care professionals.

Our review found the diagnostic performance of deep learning models to be equivalent to that of health-care professionals. However, a major finding of the review is that few studies presented externally validated results or compared the performance of deep learning models and health-care professionals using the same sample. Additionally, poor reporting is prevalent in deep learning studies, which limits reliable interpretation of the reported diagnostic accuracy. New reporting standards that address specific challenges of deep learning could improve future studies, enabling greater confidence in the results of future evaluations of this promising technology.

The full article can be downloaded below.