May 17, 2020

Natural Language Processing unlocks hidden data to transform healthcare efficiency, quality and cost

Digital health
EHR
Big Data
EHR
Anand Shroff, Chief Developmen...
6 min
The digitisation of health care has created an explosion of data. Growing patient volumes due to an aging population and greater access to health insura...

The digitisation of health care has created an explosion of data. Growing patient volumes due to an aging population and greater access to health insurance have combined with increasing adoption of electronic health records (EHR) and IoT, remote monitoring and wearable devices to spawn a massive influx of information.

This sea of data has tremendous potential to improve the quality, efficiency, and cost of healthcare. For providers, it could mean greater detail in documentation, more efficient diagnoses, and access to broader, potentially more effective, treatment recommendations. For payers, this data could provide insights that improve decisions about patient care within the confines of coverage limitations and help inform adjustments in health plan product offerings. And, for patients, it could mean more efficient access to care and more effective treatments at a lower cost.

The Data Dilemma

But, making sense of it all is a formidable challenge, and right now, payers and providers are often forced to make important decisions without access to the best possible data. The hurdle: as much as 80% of meaningful clinical data is captured in unstructured data, including physician notes, clinical observations, family and social history, etc.

Within this data lies powerful insights that both physicians and payers could use to make better decisions. The problem is that analysing this unstructured data typically requires manual, human review, which can be error-prone and resource-intensive. This productivity problem leads to immeasurable waste and operational inefficiency, missed revenue, higher costs, longer time to diagnosis and treatment, and overall diminished ability to manage patient and population health.

NLP: Solving the Productivity Problem

Natural language processing (NLP) has emerged as a promising solution to the problem, creating an efficient, effective way to understand and analyse this unstructured data. By unraveling the complexities of human language using artificial intelligence, NLP solutions can interpret the expressiveness, variety, ambiguity, and implied meaning of physician dictation, notes and other clinical unstructured data to make this data usable in making care and payment decisions at massive scale.

For health care applications, NLP’s value lies in its ability to understand grammar, syntax, context and intent, specifically around the esoteric nature of clinical language. It must understand complex medical jargon, organisation-specific language, the abbreviations and shorthand, etc., found uniquely in health care environments. For example: does the abbreviation “AF” represent the patient’s initials, atrial fibrillation, or amniotic fluid?

A clinically-oriented NLP can decipher coding based on the context within the patient’s chart, language parsing, named entity recognition, part-of-speech tagging, sentiment analysis, and disambiguation. An effective health care-specific NLP can record and document problems and negated problems with contextual attributes and even assign a degree of certainty and temporality, all automatically and in a format that can be usable for multiple applications. Here are just a few broad examples:

  • Population Health

NLP analysed data can help physicians make faster, better-informed and more accurate clinical decisions. By having access to unstructured data from multiple sources, such as lab results, office visits, and diagnostic notes across the care continuum, physicians can better identify at-risk patients, spot chronic conditions, and devise better treatment strategies based on a more comprehensive and clearer picture of the patient’s overall health and wellness. At the same time, this data can ensure more accurate coding and billing, and aid in outcomes-based/bundled payments programs, as well as regulatory reporting for quality assurance.

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  • Research

By leveraging population data analysed through NLP, research entities can gain a broader perspective when it comes to drug development and clinical trials. For example, NLP can be used to conduct pharmacovigilance for adverse event monitoring by culling through clinical reports of patients with an efficiency and speed no human analyst could match. It can also be used to assemble trial cohorts by identifying patients who meet specific criteria for participation, but who also don’t possess any disqualifying factors. In the trial process itself, NLP can make analysing all data collected exponentially faster, all of which can help to make safer, more effective drugs and other treatment therapies available much faster.

  • Coding & Documentation

NLP’s supercharged efficiency really shines when it comes to speeding documentation processes. Not only can it facilitate computer-assisted physician documentation and automate the creation of patient summaries, but it can also enable computer-assisted risk adjustment coding. This not only speeds the documentation process and ensures that all diagnoses and procedures are coded, but also enables more efficient billing and payments.

In the case of government-sponsored health programs, such as Medicare Advantage or Affordable Care Act plans, risk adjustment is a tedious, manual process by which coding specialists review records for unreported risk conditions. NLP can streamline this process by automatically reviewing every chart, alongside other EHR data and surfacing those where errors may be present. This enables systematic retrospective review of only those records where it’s truly necessary to eliminate waste, increase coder productivity and optimise ROI on the risk adjustment opportunity. At one large, provider-sponsored health plan, this process cut the number of member reviews by nearly 80 percent, generating a 4X increase in coder capacity and over $650,000 in incremental revenue.

On the flip side, NLP can also mitigate compliance risk by identifying previously claimed codes without substantiating evidence in medical records. This helps provider and payer organisations to improve accuracy and reduce waste. In one health plan case, an NLP review of records helped to identify and correct over 120 non-compliant codes.

The Reality of AI

Of course, no computer-assisted, automated or AI solution is without some risk or drawback. Now that we’ve covered some of what NLP can do, let’s be clear about a few challenges that remain.

  • Lack of EHR integration. In most cases, NLP is still a step removed from EHR and has yet to be integrated into the physician’s workflow on a large scale. Achieving that integration would dramatically accelerate manual tasks and give physicians more clinical time for direct, hands-on patient care. Current methods have physicians spending far too much time on charting and documentation, whereas integrating NLP technology could reduce much of that burden, allowing physicians to treat more patients and engage in more meaningful interactions. 

 

  • Overcoming data silos. Having access to complete data for each patient as they move through various parts of the care continuum is still a challenge. Without connected systems linking a primary care physician, specialists, hospitals, labs and more, any NLP will be limited in its capability to deliver the breadth and depth of data that can transform the health care experience. While NLP can still operate within data silos, eliminating them by connecting data can unlock even more value.

 

  • Processing quality. All NLP technologies are not created equally. There are a number of NLP technologies in place in many other industries that simply don’t translate well to a health care application, and most health-specific NLP engines aren’t built to adequately handle the rigors of healthcare. Quality of data processing is critical, and analysis must be accurate. Not only are we dealing in potentially in life-and-death decisions, but inaccurate results do more to impede the efficiency of care by forcing providers and payers to double-check everything. Health care NLP engines must be able to provide reliable performance at scale, not only in terms of processing power, but also accuracy. A well-trained, fully calibrated NLP can deliver dependable results.

A Promising Future

While NLP and other AI solutions for health care will never eliminate the need for clinician review, expertise and validation, they can certainly accelerate the process by providing a concise, yet thorough, summary of evidence that can be used to make faster, smarter treatment and payment decisions. Implementing NLP solutions can enable health care organisations to greatly improve efficiency, accuracy and access to care, thereby improving patient outcomes and overall population health.

Credit: Anand Shroff, Chief Development Officer at Health Fidelity

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Jul 25, 2021

Getting ready for cloud data-driven healthcare

Data
healthcare
CloudComputing
Technology
 Joe Gaska
4 min
Getting ready for cloud data-driven healthcare
 Joe Gaska, CEO of GRAX, tells us how healthcare providers can become cloud-based and data-driven organisations

As healthcare continues to recognise the value of data and digital transformation, many organisations are relying on the cloud to make their future-forward and data-centric thinking a reality. In fact, the global healthcare cloud computing market was valued at approximately $18 billion and is expected to generate around $61 billion USD by 2025. 

At the forefront of these changes is the rapid adoption of cloud-based, or software-as-a-service (SaaS), applications. These apps can be used to handle patient interactions, track prescriptions, care, billing and more, and the insights derived from this important data can vastly improve operations, procurement and courses of treatment. However, before healthcare organisations can begin to dream about a true data-driven future, they have to deal with a data-driven dilemma: compliance. 

Meeting regulation requirements

It’s no secret that healthcare is a highly regulated industry when it comes to data and privacy – and rightfully so. Patient records contain extremely sensitive data that, if changed or erased, could cost someone their life. This is why healthcare systems rely on legacy technologies, like Cerner and Epic EHRs, to manage patient information – the industry knows the vendors put an emphasis on making them as secure as possible.

Yet when SaaS applications are introduced and data starts being moved into them, compliance gets complicated. For example, every time a new application is introduced into an organisation, that organisation must have the vendor complete a BAA (Business Associate Agreement). This agreement essentially puts the responsibility for the safety of patients’ information — maintaining appropriate safeguards and complying with regulations — on the vendor.

However, even with these agreements in place, healthcare systems still are at risk of failing to meet compliance requirements. To comply with HIPAA, U.S. Food and Drug Administration 21 CFR Part 11 and other regulations that stipulate the need to exercise best practices to keep electronic patient data safe, healthcare organisations must maintain comprehensive audit trails – something that gets increasingly difficult when data sits in an application that resides in the vendor’s infrastructure.

Additionally, data often does not stay in the applications – instead healthcare users download, save and copy it into other business intelligence tools, creating data sprawl across the organisation and exposing patient privacy to greater risk. 

With so many of these tools that are meant to spur growth and more effective care creating compliance challenges, it begs the question: how can healthcare organisations take advantage of the data they have without risking non-compliance?

Data ownership

Yes, healthcare organisations can adhere to regulations while also getting valuable insights from the wealth of data they have available. However, to help do this, organisations must own their data. This means data must be backed up and stored in an environment that they have control over, rather than in the SaaS vendors’ applications.

Backing up historical SaaS application data directly from an app into an organisation’s own secure cloud infrastructure, such as AWS or Microsoft Azure, makes it easier, and less costly, to maintain a digital chain of custody – or a trail of the different touchpoints of data. This not only increases the visibility and auditability of that data, but organisations can then set appropriate controls around who can access the data.

Likewise, having data from these apps located in one central, easily accessible location can decrease the number of copies floating around an organisation, reducing the surface area of exposure while also making it easier for organisations to securely pull data into business intelligence tools. 

When healthcare providers have unfettered access to all their historical data, the possibilities for growth and insights are endless. For example, having ownership and ready access to authorised data can help organisations further implement and support outcome-based care. Insights enabled by this data will help inform diagnoses, prescriptions, treatment plans and more, which benefits not only the patient, but the healthcare ecosystem as a whole. 

To keep optimising and improving care, healthcare systems must take advantage of new tools like SaaS applications. By backing up and owning their historical SaaS application data, they can do so while minimising the risk to patient privacy or compliance requirements. Having this ownership and access can propel healthcare organisations to be more data-driven – creating better outcomes for everyone. 

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