Solutions enable identification of disease trends, early detection, and interventions

April 1, 2020 — Chico, CA – The solution will support efforts to identify COVID-19 disease trends and can assist the development of potential therapies. In an effort to help researchers and governments battling the COVID-19 pandemic make earlier detection and interventions possible, SyTrue, Inc., a provider of Natural Language Processing (NLP) solutions, today announced the Company will provide its SyHealth™ for Population Health solution free of charge to qualified global public health organizations. 
As increasing numbers of patients are diagnosed with COVID-19, front-line physicians will need to quickly document their encounters, oftentimes using shorthand or other methods to capture important data. SyTrue’s solutions standardize that data, making it possible to track the progress of the disease and gather crucial information about patients, such as new symptoms, risk factors, and comorbidities. SyTrue’s suite of solutions can also follow patients over time, creating longitudinal health records. 
“As the number of COVID-19 cases soars both in the U.S. and around the world, data will grow exponentially. Our current healthcare system is not equipped to review and analyze this crucial information fast enough to stay ahead of the evolving pandemic,” said Kyle Silvestro, CEO of SyTrue. “To assist those working tirelessly to battle this pandemic, our SyTrue NLP OS™ platform and solutions can help health systems, global and domestic public health organizations, health plans and researchers tap into this data and make it usable, shareable and actionable in real-time.”  
In order to harness the data that threatens to overwhelm the system, SyTrue has developed solutions that enable: 

  • Rapid Identification of Emerging Disease Trends – SyHealth™ for Population Health makes possible retrospective analysis of trends across data that are not discoverable using electronic medical records (EMRs).  Globally, SyHealth can be used to normalize data across multiple languages to speed the digitization of health data and identify emerging disease patterns. 
  • Safety-monitoring of medicinal therapies – SyWhat™ terminology server provides real-time adverse event reporting that is uniquely able to encode Medical Dictionary for Regulatory Activities (medDRA) codes and provide information that will be crucial as potential therapies for COVID-19 enter development and are tested in humans. SyWhat™ is an advanced clinical entity normalization and mapping server which can convert both structured and free-text to standardized terminologies.
  • COVID-19 Knowledge Extraction – The extraction and normalization of data from CT scans to diagnose COVID-19 with radiology tool SyRad™ that is currently used by two of the leading radiology organizations in the U.S.
  • Accelerated Identification of Patient Cohorts – Using SySearch™ discovery tool, researchers can identify patient cohorts and pandemic trends more quickly.
  • Expedite the Claims-review Process – As COVID-19 cases surge in the coming weeks to months, organizations will be able to process claims faster using with SyAudit, reducing costs and ensuring payment integrity.

For more information visit:

About SyTrue

SyTrue has developed a pioneering solution using Natural Language Processing (NLP), Artificial Intelligence (AI) and Machine Learning (ML) that has the potential to transform healthcare by reducing waste, abuse and cost. The proprietary SyTrue NLP OS™ platform and solutions including SyAudit™, SyHealth™ and SyReview™ enable health plans to extract and analyze the vast amounts of data found in medical records quickly and effectively to improve payment integrity, lower administrative expenses and increase profitability. For more information please visit:

SyTrue is pleased to announce the appointment of Cynthia Nustad to our Advisory Board. Ms. Nustad is recognized for her success as an innovative technology executive and a business leader in healthcare. With over two decades of executive management in healthcare and her success with creating and executing company growth strategies, she is a wonderful addition to SyTrue’s Advisory board.

SyTrue is an innovative healthcare technology company using NLP: Natural Language Processing to help solve access and utilization of Electronic HealthData”. Accessing EMH/EHR data for use across many needs at a Health Plans and service provider has been a hard problem to tackle until now. SyTrue helps unleash medical record content in a unique. I am excited to join this remarkable company” said Cynthia Nustad.

Ms. Nustad recently served as the EVP, Chief Strategy Officer of HMS Holdings (Nasdaq: HMSY) where she oversaw the company’s strategy, roadmap and integration of new product and technology capabilities. Nustad was also instrumental in directing the evolution and growth of corporate technology, data & analytics, software and solutions. She has spearheaded the creation of a new business verticals, creating aligned operations and integrated acquisitions with internal innovation. Her forte: the investment thesis of acquisitions, business transformation, creating scale with technology, and cultural integration, all with a special emphasis on creating long-term shareholder value.

“We are thrilled to have Cynthia on our team. She brings a tremendous amount of knowledge in Total Population Management and Payment Integrity. Cynthia’s role is critical as SyTrue roles out is SyAudit™ and SyHealth™ solutions to Health Plans and Service Providers.” said Kyle Silvestro, CEO

Previously, as EVP and CIO at HMS, Nustad led the technology, cybersecurity and product innovation functions. This helped establish HMS as a healthcare technology company and created the foundation to propel its future growth. She has led large operations with both domestic and international talent. Earlier in her career she served as a CTO, had P&L responsibility, and led product development. Serving Fortune 500, mid-size and startup companies, she has also held executive leadership roles at Cambia Health Solutions and WellPoint/Anthem (NYSE: ANTM) to name a few.

Cynthia holds an MBA from the University of Oregon, an MPH and BA from UCLA. She currently serves as a Board Advisor for Instamed, a digital banking and payments network for healthcare. She was previously a Board Member for Integriguard and a not-for-profit organization, Outside In.

WANTED: Smart “Hacks” to Boost Healthcare Data Quality


There’s a rising tide of healthcare data. It lifts many hopes for better healthcare, but also surfaces one troubling issue: reliability of data.

Just how confident are you of the reliability of your data?

See of Data

As a healthcare provider, you already know that data permeate your office workload. This impacts a critical feature of your operations: your workflow, a process you probably evolved over many years. Suddenly, you’re now doing “refreshes” to accommodate the new data volumes you’re seeing. “We’ve always done it this way” – that just doesn’t cut it any longer.

Time was when you had dictation, writing and paper records. You now have many data input options (EMRs, voice-enabled documentation and more).

So volume keeps growing, tools get more complex. Bigger yet are the issues around understanding your data, some not really obvious. For physicians, the EMR demands careful checks of patient records, new ways to capture care offered elsewhere, new diagnostic tools and ways for updating your patient’s condition — plus a bigger focus on “quality assurance.” Your “inputs” now need accuracy checks. It also means you’re the new data entry analyst on the block, and you’re burdened with an extra tall order for vigilance.

Now, how reliable are your data?

Reliable data

Example: At the point of care, as ICD codes get assigned to cases, there are some common errors, but their rates may top the 20% level (and higher still in some studies that have carefully assessed the data error issue). [Please see:] The inaccuracies may come from patient behavior, the record trail itself, the provider or physician. But errors do seep into the record: A physician uses a typical synonym to label ‘‘stroke’’: She can choose “cerebrovascular accident,” “cerebral occlusion,” “cerebral infarction” or “apoplexy.” Which is right?

Even if we correct for study differences among error-rate studies of clinical data, we know the error rates are unacceptable. The complexity of a case, provider inexperience, patients lacking skill in discussing symptoms, or the reluctance to listen to a patient’s view of her condition – all matter. It’s not uncommon to hear that even professional nurses may not be taken seriously as they describe their own condition to an emergency room provider.


If it seems special pleading to belabor the issues around data, picture:

Your child, driven from athletic field to an ER, being diagnosed for a traumatic hit to the head. Will all diagnoses work, and will you trust them?

Your aging parent, living with multiple chronic conditions, uncertain about the new pains in her body. Where in her treatment steps will you overlook “understandable” error?

At moments of truth like these, we lose patience and tolerance for any (let alone “understandable”) errors. But medicine is still catching up with us. Clinical errors still can, and do create chains of miscues that prove fatal.

The quiet fact: While errors continue to crop up – at stubborn levels and rates — we know how to minimize them at the point of care. We know the “hacks” needed to produce much better data quality, and how to use those tools. At SyTrue, we use a comprehensive data platform so that diagnoses are done right, coded right, can be queried in “natural language” terms and can yield C-CDA care records that patients will take anywhere.

Nonetheless, data errors continue to get “spiraled” into the medical record and analytics trail. So when it’s time for analytics, there’s only so much that can be extracted as the “true record” of a patient meeting. By then, it’s too late. By that point, an inaccurate diagnosis and recording errors could well be compounded by the medications and treatment used.

Simple human error shadows many issues even with data missing from the picture. In the US, remember, we still see more than 2,300 annual wrong-site, wrong-patient operations (about 46 per week). These may be “understandable”—but acceptable? Add data to this kind of picture, and it’s a volatile mix.

The healthcare system is beginning to tackle the healthcare data issue with some pace: But put very simply, the data remain unreliable. We know US medicine has many core issues, so while data quality gets mention, it doesn’t attract follow-through. Healthcare, meanwhile, correctly still targets the triple aim (better care, better quality, lower cost). It responds to practical concerns: Expensive drugs (Sovaldi) or new policies (ONC on interoperability). But data quality issues live on and may well escalate.


Healthcare’s “Holy Grail” is interoperability. It’s been missing in action while getting lots of notice in planning. But with the ONC’s new urgency to achieve interoperability by 2017 – sooner than envisioned in 2013 – we’re seeing a tough road ahead. It may mean “mountain climbing” over many hills of unreliable data, just to get to a base camp near the top.

ONC former Chief Science Officer, Douglas Fridsma, once quipped in 2013 that the US standard of interoperability is a “modem and a fax machine.”

What’s next: Our many proprietary US clinical documentation systems, each with data error levels that may not be thoroughly understood, may be asked to lead this vanguard to the “interoperability” summit. Let’s get the data issue right — before the path begins to look like a “bridge too far.” Why not fix the reliability of data and help all patients get better care?

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