Mary Ellen Cagnassola

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The Health Information Technology for Economic and Clinical Health (HITECH) Act was designed in hopes of shepherding US healthcare into the digital age. But eight years later, its unforeseen failures are burdening frustrated clinicians, whose impaired workflow has led to data blockage.

 points to five key areas — usability, workflow, innovation, interoperability, and patient engagement &mdash; critically affected by HITECH’s shortcomings. The manifestation of these deficiencies stems from aggressive “meaningful use” criteria, which was intended to ensure that Medicare and Medicaid saw the benefits of their investment in HITECH on an ambitious timeline.

Instead, the review argues, meaningful use created a strenuous onus on physicians to transform paper-based workflows to electronic health records (EHRs), on top of their duties to provide quality care to patients. For each short appointment, physicians were tasked with entering the structured data elements according to meaningful use, implementing security protections for that data, learning new billing codes and distributing new HIPAA privacy notices, all while convincing patients of the security of patient portals and e-mail. 

“In a sense, we gave clinicians suboptimal cars, didn’t build roads, and then blamed them for not driving,” the authors of the review said of the transition’s nearsightedness.

Meaningful use criteria squeezed EHR vendors, too, thanks to financial and opportunity costs imposed by complex certification requirements.

The poor usability and lack of actionable information of EHR, according to another 

review published simultaneously, has amounted to the allowance of proprietary standards and data blocking in the market, resulting in subpar data sharing. In other words, the insufficient accessibility of these hastily implemented systems — and the subsequent stress that the transition from paper-based workflow put on clinicians &mdash; is blocking the flow of data, stymying HITECH’s long-term goals.

“Instead of recognizing the work that needed to be done on these foundational items, some policymakers invented the myth of ‘information blocking’ as the root cause for lack of data flow. Our 50-plus combined years in the health IT industry have taught us that when technology, policy, and business needs are aligned, data flow,” the authors of “The HITECH Era in Retrospect” claim.

HITECH, despite its downfalls, was a necessary piece of legislation for the implementation of EHRs in the U.S and beneficial for the health care industry overall. Today, decision-making power must be returned to customers, developers, and multi-stakeholder collaborations to take some of the pressure off clinicians.

“Policymakers and the private sector now have an opportunity to ensure that the burgeoning health IT infrastructure engenders an equitable learning health system that leads to high-quality care, lower costs, and a healthy and engaged population,” the authors of “

Moving forward, a four-step approach, beginning with the simplification of meaningful use requirements, can help patch the policy holes. EHR certification should also shift focus to interoperability capabilities via a public test server and reporting on EHR vendors’ success in reading and writing medical records on it.

The remaining work to be done, according to “

,” involves encouraging interoperability through market action over regulation.

“Finally, we could offer incentives for the adoption of open industry application programming interface (API) standards, such as FHIR, for provider-patient, provider-provider, provider-payer, and payer-patient interactions,” the authors recommended.

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"We gave clinicians suboptimal cars, didn’t build roads, and then blamed them for not driving,” the authors said of the transition’s nearsightedness.

New Reviews Reflect Critically on HITECH Implementation

Researchers at the Massachusetts Institute of Technology have created a novel system to help physicians draw the line between patients with genuine pain and those who are addicted to opioids and display drug-seeking behaviors (DSB).

The new pain-measurement system goes by the moniker DeepFaceLIFT, and MIT researchers say it uses technologically-supported pain metrics to bring the medical community a step closer to remedying the DSB phenomenon.

The two-way system aims to replace the self-reported visual-analog scale (VAS), which is widely considered to be a unidimensional and context-dependent model for gauging a patient’s pain. What’s more, patients seeking narcotics can easily overstate their pain with this model, resulting in wasted time and resources for physicians.

To combat problems associated with the self-reported pain scale, DeepFaceLIFT bases its metrics on Neural Network and Gaussian process regression models in an effort to personalize the estimation of self-reported pain through hand-crafted personal features and multi-task learning.

“We show on the benchmark dataset for pain analysis (The UNBC-McMaster Shoulder Pain Expression Archive) that the proposed personalized model largely outperforms the traditional, unpersonalized models,” study authors said. “Additionally, DeepFaceLIFT automatically discovers the pain-relevant facial regions for each person, allowing for an easy interpretation of the pain-related facial cues.”

The authors used the UNBC-McMaster Shoulder Pain Expression Archive dataset containing 200 image sequences gathered from 25 subjects, totaling 48,398 frames that include the Prkachin and Solomon Pain Intensity (PSPI) scale, and observed pain index (OPI) scores per each frame. The DeepFaceLIFT method was then personalized based on a subject’s complexion, age and gender — features that the authors claim were selected based on their straightforward labeling and effect on how individuals express pain.

Despite the input of these personal features for DeepFaceLIFT’s interpretable learning model, the study does not address all potential conflicts that could arise due to differences in how individuals express pain. Patients with Autism Spectrum Disorders (ASD), chronic pain, or other conditions that determine pain response were not accounted for in the study.

Because the study only analyzed the expressions of 25 subjects from a dataset, it is likely that DeepFaceLIFT can improve on traditional pain-measurement, but is not a foolproof method for automatically estimating subjective, self-reported VAS pain scores.

“In the future, we plan to further investigate the relationships between VAS and other pain scores (such as PSPI) as well as how they relate to facial AUs,” the authors said. “As context is another important aspect not explored in this paper,     we plan to derive more advanced statistics that could potentially capture additional information and improve estimates of subjective pain.”

With the goal of advancing applications of pain estimation in clinical settings, the authors plan to continue advancing DeepFaceLIFT’s personalization process by expanding its recognition capabilities.

DeepFaceLIFT: Interpretable Personalized Models for Automatic Estimation of Self-Reported Pain

The novel tech represents an improvement on the visual analog scale, but does not account for several patient variables.

Mary Ellen Cagnassola

Articles

New Reviews Reflect Critically on HITECH Implementation

Can DeepFaceLIFT Replace Old Models for Identifying Pain?