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Pharma
Biotech

Bone deformities, hearing loss, frequent respiratory infections, cognitive impairment and chronic heart and liver disorders are symptoms suffered by infants with Hunter syndrome (also known as Mucopolysaccharidosis II). This blog follows our previous blog on associations between genotype and phenotype in very rare diseases, carried out by Shire. 

Shire, now part of Takeda, provides an enzyme replacement therapy for Hunter Syndrome. However, in order to ameliorate the neurocognitive effects, the enzyme replacement molecule needs to be delivered to the central nervous system (CNS) via an innovative implant device, which is an invasive procedure.

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Biotech
Pharma

In her article in Rx Data, Jane Reed, Director Life Science at Linguamatics, discusses the impact of advanced data technologies (artificial intelligence and machine learning) on innovation in drug discovery, development and delivery.

We are now in the fourth industrial revolution (4IR), known to some as the Big Data Revolution. Advances in connectivity and communication, in the digital revolution, bring results such as improved data access and the new-found potential to analyze huge volumes of data. The ability to access these important volumes of varied data and to connect, integrate, query and analyze it is enabling fundamental changes in how we envision drug discovery and delivery in the clinic. Additionally, the pace of these changes is also remarkable; Jane notes a few examples of some genome-based projects and the fast-paced evolution, from the first human chromosome sequenced in 1999; the human genome published in draft in 2001, to the more recent UK 100k Genome project.

According to Jane, the key components for these innovations include data integration and data analysis. To keep up with that rhythm, pharma companies now need to join up genomic data with clinical information and knowledge about particular diseases.

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Academic Medical Center
Cancer Center
Payer
Provider

Physicians at breaking point

Unsurprisingly, physicians who are constantly under peak pressure have the highest rate of burnout with an average of 45.8%. However, the source states emergency physicians claim a whopping 60% burnout rate. I also recently received an unverified Tweet about the life expectancy for physicians in this specialty, and the news just gets worse. It’s almost 20 years less than other specialties. I am unsure if it’s that much however, if you have ever ventured into an emergency department you can see for yourself why this may be true.

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Academic Medical Center
Payer
Provider

NCQA Digital Summit workshop - streamlining HEDIS reporting with NLP

Fractured Fairy Tale - the Price of Quality

Recently, an esteemed colleague pointed out an eye-opening research article to me when we were on the subject of Quality Measures and the expenses that occur in the digital age: "US Physician Practices Spend More Than $15.4 Billion Annually To Report Quality Measures".

This article was published in 2016, however I am willing to wager that this annual expense has not gone down in the past couple of years. This expense is accrued by not only hospital care organizations (HCOs) but by the insurance companies (payers) as well, all in the name of trying to make our population healthier.

We know that time equates to money in the workforce. How much time does this reporting take on the clinical side in addition to required duties for patient care? No wonder we are facing a clinician burnout epidemic. Medscape’s 2019 report determined that 44% of physicians described themselves as being burned out. And this report only mentioned the physicians, on nursing.org statistics nurses only reported a burnout rate of 15.6%. Which almost sounds like a relief until you learn 41% of nurses reported they felt “unengaged”. How frightening would it to be to be under the care of a nurse that was “checked-out” of his/her job? Burned-out and checked out. How do we achieve better outcomes this way? And how do we know payers are obtaining the correct information from the clinical staff?

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Payer
Provider

Patient safety is an issue that healthcare organizations (HCOs) must prioritize – but how can they improve efficiency when it comes to reviewing the 80% of relevant patient information that is locked in unstructured data?

Under pressure to provide value-based care and adhere to quality measures, HCOs are increasingly turning to AI-based technologies such as Natural Language Processing (NLP), which makes unstructured data usable – thereby improving the efficiency of quality initiatives, quality measure reporting and, most importantly, patient safety.

Addressing the healthcare safety and quality challenge with NLP

While the U.S. health system has made progress in recent years, patient safety continues to be a challenge that all HCOs must prioritize. An estimated 1.7 million healthcare-associated infections occur each year in the U.S. leading to 99,000 deaths. Moreover, adverse medication events cause more than 770,000 injuries and deaths each year at a cost as high as $5.6 billion annually, according to statistics cited by the Center for Patient Safety.

NLP workflows can help reduce the likelihood of error and improve patient safety by automating the identification and extraction of key concepts from large volumes of clinical documentation. Findings are transformed into structured data to simplify chart review and speed the identification of high-risk patients.

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