7- DATA, DATA,& MORE DATA IN HEALTHCARE by PHARMAGEEK

The linked interview gives us all in Healthcare Technology  a lot of food for thought. This below is a set of points extracted and massaged with my viewpoints

 

The biggest barrier to physicians having the most complete medical history for their patients at every point of patient care is the lack of interoperability among information systems.

 

State of Interoperability today:

 

The industry has made progress in developing open standards and application programming interfaces to facilitate data fluidity and sharing among multiple electronic health record systems and data repositories. As a result, commercial and open source interoperability services are coming online. While there is room for optimism, the industry is still grappling with data structure and management challenges

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First, incomplete, disparate and disconnected data.

Most health and patient data is stored as unstructured medical format, and identifying information in the data is a manual and time-consuming process. There are significant variations in the way data is shared, read and understood across health systems, which can result in information being siloed and overlooked or misinterpreted

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Further, most EHR systems do not follow patients on their care journey beyond the hospital or clinic walls. As a result, only a portion of healthcare data is available at any point of care, resulting in a fragmented view of a patient's health history.

 

 

Second, slow adoption and scaling of open interoperability standards.

Standards can streamline the structured data exchange needed to improve preventive and value-based care for people, predictions, diagnostics, post-marketing surveillance of medical products (for example drug, device), care quality, cost reduction and clinical research.

Industry guidelines and resources like the Fast Healthcare Interoperability Resources (FHIR) from Health Level Seven International (HL7) have helped to set a standard, though there is still more work to be done to support organizations to remove barriers toward adoption and make the electronic exchange of data more seamless, with the goal of providing a better provider and patient experience.

 

Third, risks due to siloed data:

When it comes to storing health information including clinical, genomic, device, financial, supply chain and claims, data security is the top priority. Storing patient data across different systems and platforms makes it difficult to deliver personalized care, draw data insights and streamline service.

 

This is a pivotal moment in time when healthcare can take what it's learned over the past year and fix the underlying problems.

 

Perhaps the most important learning is that achieving true healthcare interoperability requires understanding, evaluating and solving issues in the underlying syntactic and semantic characteristics of the data. 

 

Syntactic interoperability requires a common structure so that data can be exchanged and interpreted between health IT systems,

while semantic interoperability requires a common language so that the meaning of data is transferred along with the data itself. This combination supports data fluidity.

 

The industry has made meaningful progress on this front.

 

Unlocking Benefits of Interoperability

 

As technology creates more data across healthcare organizations, applying technologies like artificial intelligence and machine learning will be essential to help take that data and create the shared structure and meaning necessary to achieve interoperability.

 

Shared structure and meaning will enable interoperability solutions that transform data input from various media types and forms: voice, image, scan, PDF, etc., into a common text format which can be shared with and leveraged by every entity in the value chain.

Instead of moving static, electronic documents or faxes like care summaries between healthcare providers, clinical AI-service APIs can enable EHR vendors and health systems to communicate in a standardized way with apps and other EHRs.

 

With access to all available information, advanced analytics and machine learning can then enhance medical and scientific insights tied to patient outcomes in an accurate, scalable, secure and timely manner.

 

 

read more at https://www.healthcareitnews.com/news/amazon-web-services-exec-talks-interoperability-lessons-past-year

 

Via nrip

IBM Watson Health is teaming with the American Diabetes Association to apply cognitive computing to the ADA's 66 years worth of research and data. The results will be used to help entrepreneurs, developers, healthcare providers, and patients learn more about diabetes, prevention, complications, and care

 

In 2012, according to the ADA, 29 million people were living with the disease, and another 86 million were diagnosed with a condition known as prediabetes.

 

To address the challenge, IBM Watson Health and the ADA are collaborating to apply Watson cognitive computing to the organization's massive library of information and data. Through this effort, IBM and ADA hope to empower entrepreneurs, developers, healthcare providers, and patients to gain knowledge that can improve outcomes and even prevent the condition's onset.

 

First, IBM's AI platform will ingest all the medical journals, medical text books, Pub Med, and other diabetes literature and resources available, including all the content from the ADA's Diabetes Information Center. Second,  Watson will ingest the ADA's diabetes data sets. 

 

Watson will be trained to understand the diabetes data to identify potential risk factors and create evidence-based insights that can be applied to health decisions.

 

IBM also is collaborating with the Health Maintenance Organization Maccabi Healthcare services to build a predictive machine learning model to help identify early risks for diabetic retinopathy, the top cause of blindness for those with diabetes.

 

Via nrip

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/

 

Via nrip