healthcare technology

As data and analytics are increasingly leveraged in various aspects of the healthcare system, some companies are  making use of such capabilities to help clinicians make the best decisions for patients.

One such company is naviHealth.  Based in Brentwood, Tennessee, naviHealth provides both payers and providers with post-acute care management expertise. Its nH Predict tool allows clinicians to better predict a patient’s outcomes in order to craft a personalized post-acute care plan.

Using NaviHealths nH Predict tool, clinicians are better able to predict a patient's outcomes and generate a personalized post-acute care plan.

The result of the tool is a simple outcome report that is generated at the beginning of the patient’s stay in a facility or hospital. The report breaks down the patient’s basic information as well as how they’re doing in a variety of categories.

For instance, nH Predict outlines the individual’s gender, date of birth and admission date. It also includes their primary diagnostic group (such as COPD) and their usual living setting (like at home alone or in an assisted living facility).

Finally, the outcome report provides a score for a few of the patient’s functions based on the data of similar patients. It gives a score on the patient’s basic mobility (such as wheelchair skills or ability to take the stairs); daily activity (like bathing and dressing); and applied cognition (including memory and communication).

Additionally, the report creates a total average score for the patient based on their mobility, activity and cognition scores.

read the complete story at https://medcitynews.com/2018/10/navihealth-data-patient-outcomes/

At the World Medical Innovation Forum this week, participants were polled with a loaded question:

“Do you think healthcare will become better or worse from the use of AI?”

Across the respondents, 98 percent said it would be either “Better” or “Much Better” and not a single one thought it would become “Much Worse.” This is an interesting statistic, and the results were not entirely surprising, especially given that artificial intelligence was the theme for the meeting.

This continual stream of adoption of new technologies in both clinical and post clinical settings is remarkable. Today, healthcare is a technology operation. As a case in point, outside of the array of MDs and medical professionals presenting at the forum, there was clearly a strong, advanced technology thread weaved throughout the conversations of the traditional topics of pathology, radiology, bioinformatics, electronic medical records (EMR), and standard healthcare provider issues.

As an example, a panel of senior technology experts from Microsoft, Cisco Systems, Dell EMC, Qualcomm, and Google joined research and information officers from Partners Healthcare and Massachusetts General Hospital to discuss the challenges in what they called “Data Engineering in Healthcare: Liberating Value.” That is a serious title for a panel.

Data portability was clearly a key topic, as was security and the public cloud.

The underlying issue with the cloud is that the EMR was never really designed to be portable.

Health records existed with institutional walls, and were not originally intended for real time care, but more as a means of tracking costs and transactions as the patient traveled through the various systems. As the EMR has not only become more feature rich, the ability to mine that data inside of them with ML and AI methods is clearly at the forefront of everyone’s mind right now.

There was discussion of episodic systems wrapped in policy and technology – this really isn’t quite how we can gain the maximum knowledge from the healthcare version of a Digital Me. A digital object containing all of our many and varied health related attributes. The challenges of discussing how to best build a “marketplace” and healthcare data exchanges and how to integrate “data marts” with existing EMR systems was obvious.

After 3 years as head of IBM’s health division, Deborah DiSanzo is leaving her role.

A company spokesman said that DiSanzo will no longer lead IBM Watson Health, the Cambridge-based division that has pitched the company’s famed artificial intelligence capabilities as solutions for a myriad of health challenges, like treating cancer and analyzing medical images.

Even as it has heavily advertised the potential of Watson Health, IBM has not met lofty expectations in some areas. Its flagship cancer software, which used artificial intelligence to recommend courses of treatment, has been ridiculed by some doctors inside and outside of the company. 

And it has struggled to integrate different technologies from other businesses it has acquired, laying off employees in the process.

more at https://www.statnews.com/2018/10/19/head-of-ibm-watson-health-leaving-post/

Information technology has allowed much of our economy to automate processes. We have seen transformations of the airline, banking, brokerage, entertainment, lodging, music, printing, publishing, shipping and taxi industries through the availability of massive volumes of real-time price and service data. Across America, consumer-facing retail service continues to shift to a virtual environment.

Healthcare is the exception. Many health information technology (health IT) products initially focused on billing.

The misalignment between billing support and the sense that these tools do not materially automate clinician work to build in efficiencies or improve workflows adds to an overall frustration with the increasing amount of time providers spend at their screens.

Automation is hard because it tends to require interfaces of various types – both to other machines (Internet of Things) and to humans.

Often automation proposals involve solutions that focus on highly structured data. But, someone or something has to put energy (physician salary, for example) into organizing much of this information, assuming it is even knowable.

The underlying disease or patient behavior (e.g., smoking) is also often not knowable. And, automation relying on machine to machine interfaces regularly runs into a lack of application programming interfaces (APIs) supporting complex clinical data flows.

I posted this week old piece now and now when it was published as #NHITWeek is this week. A lot of posts this week deal with possibilities and problems with healthcare focussed automation. 

The original unedited piece can be read at https://www.himss.org/news/healthcare-automation-transforming-medicine

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/