healthcare technology

Electronic health records are overloading outpatient docs with info in "disparate files and folders rather than presenting comprehensive, actionable data in a context that gives meaning," say researchers in a VA-funded study

 

A primary care physician may care for 2,500 or more patients in a given year, and many of their patient encounters may last only 20 minutes – much of which is often spent at a computer with a back turned to the patient.

 

It's become a truism by now that electronic health records are often viewed askance by primary care docs, many of whom see them as detrimental to the patient encounter.

 

A new report from U.S. Department of Veterans Affairs, Regenstrief Institute and Indiana University details just how outpatient EHRs are often failing the physicians who use them.

 

Why it matters

EHRs "are not rising to the challenges faced by primary care physicians because EHRs have not been designed or tailored to their specific needs,"

 

The report draws on eight years of close study of EHR use patterns to argue for wider acceptance of "human factor approach for the design or redesign of EHR user interfaces."

 

many EHRs as currently configured make it too difficult for primary care docs to do their job in a streamlined and efficacious manner

 

– requiring navigation through multiple screens and tabs to find basic information, increasing redundancy and decreasing efficiency.

 

Something as simple as auto-save – a default capability for most online shopping, for instance – is missing from many EHR systems.

 

Roots of the problem

The study traces the roots of the challenge to the fact that many EHRs were initially designed for specialists and hospitals – without much attention to the specific needs of primary care physicians.

For Primary care physicians, effective decision-making is grounded in perception and comprehension of a patient's dynamic situation."

 

For example, they note, an outpatient doc's choice to stop a patient's use of a particular medication will usually be informed by trends in that patient's blood pressure or cholesterol numbers, or other medications taken over the course of a month – all holistic information with implications for the patient's future health trajectory, but data that isn't always readily seen on a single EHR screen.

 

Technology needs to adapt to humans' needs, abilities, and limitations in healthcare delivery as it has in other domains.

 

EHRs should be redesigned to improve situational awareness for busy primary care physicians and support their tasks including reviewing patient information, care coordination, and shared decision-making."

 

read more at https://www.healthcareitnews.com/news/regenstrief-study-shows-ehrs-underperforming-primary-care

 

 

Web-based technology has dramatically improved our ability to detect communicable disease outbreaks, with the potential to reduce morbidity and mortality because of swift public health action.

 

Apps accessible through the internet and on mobile devices create an opportunity to enhance our traditional indicator-based surveillance systems, which have high specificity but issues with timeliness.

Objective: The aim of this study is to describe the literature on web-based apps for indicator-based surveillance and response to acute communicable disease outbreaks in the community with regard to their design, implementation, and evaluation.

Results: Apps were primarily designed to improve the early detection of disease outbreaks, targeted government settings, and comprised either complex algorithmic or statistical outbreak detection mechanisms or both.

 

We identified a need for these apps to have more features to support secure information exchange and outbreak response actions, with a focus on outbreak verification processes and staff and resources to support app operations.

 

Conclusions: Public health officials designing new or improving existing disease outbreak web-based apps should ensure that outbreak detection is automatic and signals are verified by users, the app is easy to use, and staff and resources are available to support the operations of the app and conduct rigorous and holistic evaluations.

 

read the study at https://publichealth.jmir.org/2021/4/e24330

 

The Food and Drug Administration is proposing to pre-certify vendors of certain medical device software, including various mobile apps, allowing the companies to skip the agency's much more rigorous pre-market approval process for hardware-based medical devices.

 

The proposed voluntary program is for review of "software-as-a-medical-device" products, or SaMD - software that is "intended to treat, diagnose, cure, mitigate or prevent disease or other conditions." Today, such software faces the same regulatory review as medical device hardware.

 

Examples of SaMD range from software that allows a smartphone to view images obtained from a MRI for diagnostic purposes to computer-aided detection software used to help detect breast cancer.

 

The FDA says its current regulation of medical device hardware "is not well-suited for the faster, iterative design, development, and type of validation used for SaMD," according to the agency's draft "working model" document spelling out its proposals.

 

more at: https://www.govinfosecurity.com/fda-unveils-plan-for-software-as-medical-device-review-a-10925

 

The use of technology in health care has proven to increases care providers, the capabilities and to patients, access to improved quality of healthcare. During the GDD summit organized by BAO Systems, it occurred that some of the most beneficial countries include Nigeria.

 

Speaking at the summit, InStrat Global Health Solution (InStratGHS) CEO, Okey Okuzu, discussed the design, deployment, and impact of Artificial Intelligence (AI) tool – EWORS for TB case finding in Nigeria. EWORS is built on the MediXcel Digital Health Platform by Plus91

 

“Our goal is to overcome barriers to healthcare delivery in low resource settings presented by infrastructural challenges,” he noted. “We achieve this by leveraging mobile health technologies that allow for smooth flow of information across the health system for more effective patient health management or policy decisions regardless of physical location.”

 

The MediXcel-EWORS system is designed to drive actionable AI for public health intervention by sieving through a large volume of data to enable specific geospatial identification of disease cases and also predict the possibility of an outbreak based on historical data and set algorithm threshold.

 

This helps local surveillance personnel to make data-informed decisions to curb the spread of diseases. Field data, captured on android devices wirelessly transferred to a cloud server for storage and analysis.

 

“The EWORS engine conducts advanced analytics to detect unexpected elevations in indicator data and populates this information on real-time GIS heat maps, reports, and alert notifications,” he added.

 

The alert notifications are generated in form of emails/SMS and sent to designated individuals when data from local areas exceed set thresholds, indicating undetected community spread. Designated teams review alarms and mobilize to conduct mass screening outreach at Alarm locations.

 

Under a USAID-funded program led by the KNCV TB Foundation, and in partnership with Plus91 PVT, InStrat GHS deployed its EWORS predictive engine to 14 Nigerian states as a solution to find undetected TB cases in the country. The Analyses of the data from program inception to date demonstrate that prioritization of case finding outreach efforts, based on hotspot analytics and alarms, increases the yield of those efforts.

 

This strategy is crucial to finding missing TB cases and improving case-finding, especially in low resource settings,

 

read more at https://www.cio.co.ke/role-of-ai-in-healthcare-tb-prevalence-in-nigeria/

 

read about Instrats at https://instratghs.com/

 

read about Plus91 and MediXcel at https://plus91.in/about-us/

 

read about KCNV at https://kncvnigeria.org/

 

It might not be long before algorithms routinely save lives—as long as doctors are willing to put ever more trust in machines.

 

An algorithm that spots heart arrhythmia shows how AI will revolutionize medicine—but patients must trust machines with their lives.

 

A team of researchers at Stanford University, led by Andrew Ng, a prominent AI researcher and an adjunct professor there, has shown that a machine-learning model can identify heart arrhythmias from an electrocardiogram (ECG) better than an expert.

 

The automated approach could prove important to everyday medical treatment by making the diagnosis of potentially deadly heartbeat irregularities more reliable. It could also make quality care more readily available in areas where resources are scarce.

 

The work is also just the latest sign of how machine learning seems likely to revolutionize medicine. In recent years, researchers have shown that machine-learning techniques can be used to spot all sorts of ailments, including, for example, breast cancer, skin cancer, and eye disease from medical images.

 

more at : https://www.technologyreview.com/s/608234/the-machines-are-getting-ready-to-play-doctor/