healthcare technology

Using electronic health records (EHRs) to create a learning healthcare system, say the authors, can enable researchers to generate new knowledge that will accrue benefits to future generations of patients.

Real-world data are increasingly viewed as a crucial factor in the eventual acceptance of biosimilar drugs, and indeed, current real-world evidence points to the safety and efficacy of these products in the marketplace.

In a recent paper, officials from the European Medicines Agency, the Organisation for Economic Cooperation, and other European government entities explained that such data can help make decisions about pharmaceuticals—from development to reimbursement—more efficient.

The authors called for international cooperation on a learning healthcare system that will better harness these data. 

The authors note that the expense of prospective data generation in a research setting is high, limiting the number of research questions that can be answered in a randomized controlled trial (RCT).

RCTs are rarely large enough to detect infrequent outcomes, nor are they long enough to determine long-term outcomes. 

Using electronic health records (EHRs) to create a learning healthcare system, say the authors, can enable researchers to generate new knowledge that will accrue benefits to future generations of patients.

However, current inadequacies of EHRs present a “technical bottleneck” to the objective of gathering real-world data.  

The paper’s authors propose that governments establish and implement national health data governance frameworks to encourage the use of personal health data to serve the public interest.

The collection of data must translate into the production of useful evidence.

A coordinated and international effort will be key to speed the implementation of a true learning healthcare system for global benefit. 

read the news article at https://www.centerforbiosimilars.com/news/european-officials-promote-use-of-ehrs-to-gather-real-world-data-on-pharmaceuticals--

The face of medical care is rapidly changing thanks to major advancements in the capture, proliferation, and analysis of medical data. Technologies like the electronic health records (EHRs) and personal health records (PHRs) are drastically improving the way data is aggregated and shared.

Now the hope is that big data analytics will help to make sense of seemingly endless streams of medical information.

As many doctors are painfully aware, outcome-oriented care is no longer a buzzword but a reality. The Center for Medicare and Medicaid Services has started to implement a program where payments are based on the ability of providers to meet key National Quality Strategy Domains (e.g. care criteria). Public payers are testing this new methodology, and private payers are expected to soon follow.

These big data analytics applications can also be relevant for the FDA, which may want to see how drugs perform in a non-test environment to ensure the appropriate patient populations are receiving the drug. I also expect pharmaceutical companies to actively scour this data to track drug efficacy post-release or identify markets that could “benefit” from increased penetration.

I am eager to see how the data evolution improves outcomes for doctors and patients.

Duke University Medicine is using geographical information to turn electronic health records (EHRs) into population health predictors. By integrating its EHR data with its geographic information system, Duke can enable clinicians to predict patients' diagnoses.

According to Health Data Management, Sohayla Pruitt was hired by Duke to run this project; “I thought, wow, if we could automate some of this, pre select some of the data, preprocess a lot and then sort of wait for an event to happen, we could pass it through our models, let them plow through thousands of geospatial variables and [let the system] tell us the actual statistical significance,” Pruitt says. “Then, once you know how geography is influencing events and what they have in common, you can project that to other places where you should be paying attention because they have similar probability.”

iHealth Beat explains that the system works by using an automated geocoding system to verify addresses with a U.S. Postal Service database. These addresses are then passed through a commercial mapping database to geocode them. Finally, the system imports all U.S. Census Bureau data with a block group ID. This results in an assessment of socioeconomic indicators for each group of patients.

“When we visually map a population and a health issue, we want to give an understanding about why something is happening in a neighborhood,” says Pruitt. “Are there certain socioeconomic factors that are contributing? Do they not have access to certain things? Do they have too much access to certain things like fast food restaurants?”

Duke is working to develop a proof of concept and algorithms that would map locations and patients. They are also working on a system to track food-borne illnesses.

As tech giants like Apple, Google and Samsung compete over dominance in the healthcare market, with their latest platforms and apps, the question remains will any or all of these innovations truly tap into the greater realm of consumer health?  Aside from the buzz now emanating from consumers’ pockets, is there a real signal being sent out here about how to change healthcare, or will these latest consumer-concentric technologies add nothing more than noise?

Welltok's Scott Rotermund explains that what we really need from Google Fit and HealthKit is an integrated approach that not only collects data, but also meshes with the current healthcare ecosystem.

What lessons learned from the disappointments associated with the consumer use of FitBit and others tell us about behavior change?

We’ve learned two things:

1. A cool gadget is not enough – The novelty will wear off along with the use of the wearable. In my experience, most people lose interest in their tracking device after a month – they learn their sleep patterns, know average steps, etc. To maintain usage, we’ve tied tracking devices to challenges, participatory incentives and personalized action plans with defined goals.

2. Relying on the consumers to take action – With the startling statistics associated to preventable diseases like obesity and diabetes, it’s safe to say that we cannot expect consumers to take action on their own. These big tech giants are treating health activities as they would consumer electronics – the same rules don’t apply. To move beyond early adopters, they need to tie into programs that provide personalized guidance on how to use the trackers and the resulting data, and align those defined actions with the right incentives to get people moving.

more at http://hitconsultant.net/2014/07/22/is-integration-the-key-to-google-fit-and-healthkits-success/

Olive automates repetitive tasks and can match patients across databases at different hospitals

When Sean Lane, a former NSA operative who served five tours of duty in Afghanistan and Iraq, first entered into the health care-AI arena, he was overwhelmed with data silos, systems that don’t speak to each other, and many, many portals and screens.

“I was not going to create another screen,” Lane told a packed room on Monday at ApplySci’s annual health technology conference at the MIT Media Lab in Cambridge, Mass. 

Instead, Lane and a team taught an AI system to use software that already exists in health care just like a human would use it. They named it Olive.

“Olive loves all that crappy software that health care already has,” said Lane. “Olive can look at any software program, any application for the first time she’s ever seen it, and understand how to use it.”

For example, Olive navigates electronic medical records, logs into hospital portals, creates reports, files insurance claims, and more.

Olive does so thanks to three key traits. First, using computer vision and Robotic Process Automation, or RPA, the program can interact with any software interface just as a human would, opening browsers and typing. Second, machine learning enables Olive to make decisions the way human health care workers do. The team trained Olive with historical data on how health care workers perform digital tasks, such as how to file an insurance eligibility check for a patient seeking to undergo a procedure.

Finally, Olive relies on a unique skill that Lane developed based on his work at the NSA identifying criminals across disparate government sources—the ability to match identities across databases. Just as NSA software can determine if a terrorist in the CIA database is the same as in the Homeland Security database, so Olive matches a patient across disparate databases and software, such as multiple electronic health care record programs.

Read the full article at https://spectrum.ieee.org/the-human-os/computing/software/this-healthcare-ai-loves-crappy-software

When an imaging run generates 1 terabyte of data, analysis becomes the problem

Today's neuroscientists have some magnificent tools at their disposal. They can, for example, examine the entire brain of a live zebrafish larva and record the activation patterns of nearly all of its 100,000 neurons in a process that takes only 1.5 seconds.

The only problem: One such imaging run yields about 1 terabyte of data, making analysis the real bottleneck as researchers seek to understand the brain.

To address this issue, scientists at Janelia Farm Research Campus have come up with a set of analytical tools designed for neuroscience and built on a distributed computing platform called Apache Spark. In their paper in Nature Methods, they demonstrate their system's capabilities by making sense of several enormous data sets. (The image above shows the whole-brain neural activity of a zebrafish larva when it was exposed to a moving visual stimulus; the different colors indicate which neurons activated in response to a movement to the left or right.)

The researchers argue that the Apache Spark platform offers an improvement over a more popular distributed computing model known as Hadoop MapReduce, which was originally based on Google's search engine technology. 

more at http://spectrum.ieee.org/tech-talk/biomedical/imaging/can-computing-keep-up-with-the-neuroscience-data-deluge/

It's been tough to identify the problems that only turn up after medicines are on the market. An experimental project is now combing through data to get earlier, more accurate warnings.

No one likes it when a new drug in people's medicine cabinets turns out to have problems — just remember the Vioxx debacle a decade ago, when the painkiller was removed from the market over concerns that it increased the risk of heart attack and stroke.

To do a better job of spotting unforeseen risks and side effects, the Food and Drug Administration is trying something new — and there's a decent chance that it involves your medical records.

It's called Mini-Sentinel, and it's a $116 million government project to actively go out and look for adverse events linked to marketed drugs. This pilot program is able to mine huge databases of medical records for signs that drugs may be linked to problems.

The usual system for monitoring the safety of marketed drugs has real shortcomings. It largely relies on voluntary reports from doctors, pharmacists, and just plain folks who took a drug and got a bad outcome.

"We get about a million reports a year that way," says Janet Woodcock, the director of the FDA's Center for Drug Evaluation and Research. "But those are random. They are whatever people choose to send us."

Big data offers breakthrough possibilities for new research and discoveries, better patient care, and greater efficiency in health and health care, as detailed in the July issue of Health Affairs. As with any new tool or technique, there is a learning curve.