Not My Health Records | A New Era of EHR Fraud

“It’s all the time. Often, once a day or more.”

Aaron Miri, CIO of Dell Medical School and University of Texas Health, on how often he’s pitched by healthcare IT companies trying to get access to patient data.

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  • Medmo – Helping underinsured Americans save on medical scans by connecting them to imaging providers with unfilled schedule time
  • Nuance – AI and cloud-powered technology solutions to help radiologists stay focused, move quickly, and work smarter
  • POCUS Systems – A new Point of Care Ultrasound startup, combining a team of POCUS veterans with next-generation genuine AI technology to disrupt the industry
  • Qure.ai – Making healthcare more accessible by applying deep learning to radiology imaging

The Imaging Wire

Not My Health Records

The Australian government’s $1.7 billion ($1.185 USD) My Health Record (MHR) platform was in the news last week after its first-year statistics revealed extremely low adoption – and radiology is partially to blame:

How Low? – Only 9% of Aussies logged on to their digital health record accounts last year and just 4% accessed their records more than once, while a minuscule 8% of specialist practices are connected to the system. Given that Australia automatically enrolled nearly all citizens in the My Health Record platform in early 2019, this isn’t very good adoption.

Specialists Left Out – It could be argued that the My Health Record platform’s low patient adoption is more of a patient behavior issue than a technological issue, but industry insiders also blame radiology and pathology software incompatibilities. Only 41% of radiologists and pathologist are connected to MHR (vs. nearly 90% of GPs), so most radiology and pathology results are not available on the platform.

Compatibility Solution – To fast-track My Health Record’s compatibility with key radiology and pathology solutions, the Australian government has resorted to paying medical software companies as much as $1m AUD to develop compatibility into their existing software.

A New Era of EHR Fraud

“The federal government funneled billions in subsidies to software vendors who overstated or deceived the government about what their products could do.” This is how KHN and Fortune started a recent article on how EHRs are creating “a new era of healthcare fraud.”

Lawsuits – The article detailed dozens of lawsuits, that followed the same general pattern. EHR companies received millions in subsidies from the U.S. government, while allegedly covering up software defects, before rolling out their potentially dangerous and/or inefficient software to doctors and hospitals.

There’s More – In addition to these cases, the government reportedly paid hundreds of millions of dollars in EHR-adoption subsidies to doctors and hospitals that couldn’t prove they were qualified to receive them, while 28% of doctors and 5% of hospitals who said they met government standards later failed audits.

Gaming the System – In addition to concealing flaws, KHN/Fortune reported that EHR companies “allegedly gamed the system, repeatedly” during the certification process. These are more than just allegations, as three major EHR vendors have already agreed to $357 million in settlements for rigging/gaming the government’s certification test and two more are still being investigated.

All Together – The mounting number of lawsuits and investigations related to flawed EHR software and questionable practices has KHN/Fortune suggesting “that a substantial chunk of the $38 billion in federal [EHR] subsidies went to companies that deceived the government about the quality of their products.” The government now gets what’s happened and is apparently taking action, but all of this software is still on the market and it’s not easy to change once adopted.

The Wire

  • A University of Michigan team developed a new approach to analyzing medical images for cancer treatment trials that could reduce result-altering errors, while easing oncologists’ workload. In an effort to address individual variability and unintentional bias that happen when oncologists interpret scans, U-M created its TRAC approach (tumor response assessment core) in 2016, which involved creating a new image analyst role, adopting a new image review workflow (initial reviews by image analyst, follow up reviews by specialized radiologists), and adding an outside review process to resolve review disagreements or ambiguities.
  • A recent CNBC article detailed how hospitals are being flooded with requests for patient health data from healthcare IT companies. The article included executive quotes from some major hospital systems (University of Texas, Jefferson Health), cited some big-name tech companies (Amazon, Google, Flatiron Health, IQVIA), and revealed some concerning details on how patients can be identified from this data. This may be old news to many Imaging Wire readers, but it’s new news to most CNBC readers and should increase the role of patient data privacy in the national conversation.
  • OXOS Medical’s forthcoming Micro C handheld X-ray and photography hybrid system was recently profiled, revealing that the unique device is approaching FDA approval and is set to launch in 2020. Invented by an orthopedic microsurgeon, the Micro C takes X-rays and photographs to help guide surgeries, and is positioned as a C-Arm replacement due to its “faster, safer, clearer” value proposition.
  • A recent JACR article detailed how Brigham and Women’s Hospital overcame radiology’s historic challenge maintaining gender balance (U.S. overall women 27% of radiology residents vs. BWH ~40% of residents) and shared some steps that other hospitals can follow to create similar programs. Here are the steps: 1) Create solid roles and structure (governance, trainee leadership, faculty sponsors); 2) Draft a sound proposal to hospital leadership (mission statement, calendar, budget); 3) Maintain communication (email, social media); 4) Keep a calendar of program events; 5) Make sure the program is sustainable (get groups involved, ensure funding); 6) Measure program results to show how it improved the radiology department’s gender balance.
  • A UC Riverside bioengineering professor received a $300,000 grant to develop a dual imaging technique that improves staging and localization of ovarian cancer before and during surgery. The new technique uses a fluorescent dye embedded in a nanosized liposome to measure tumor nodules (smaller than 1mm) and spot tumors to remove during surgery, combined with a MR agent in the same liposome used to determine tumor stage and location with MRI imaging.
  • RSNA and Carequality rolled out the Imaging Data Exchange Implementation Guide Supplement, which adds new medical image exchange standards to the Carequality Interoperability Framework. The new guide comes as a result of RSNA’s partnership with Carequality and the Sequoia Project, and lists Ambra Health, Life Image, and Philips Healthcare as early adopters who will encourage their users to share images within the framework.
  • Healthcare Administrative Partners shared the latest on how surprise billing laws have developed at the state-level and how radiology practices across the country should prepare for the expected federal legislation. Although 9 states already have comprehensive surprise billing laws and 16 states ensure partial protection, HAP details the changes that are likely to come from new federal surprise billing legislation, while sharing some useful guidelines on how radiology practices should prepare for these changes (collaborate with hospital to inform patients of ONN status and costs, take efforts to comply with laws in every state).
  • The role and risks of healthcare AI took center stage at the close of 2019, as Nature.com and Scientific American both published lengthy stories on these two sides of healthcare AI (with a primary focus on imaging AI). The Scientific American story, written by KHN, detailed healthcare AI’s evolution and potential, but mainly detailed its risks (bias, misleading results, relaxed FDA standards) and lack of supporting scientific evidence (no randomized trials, black box design). The Nature.com article took a more positive tone, placing a greater focus on healthcare AI’s benefits and long-term potential, but still discussed a range of barriers to AI reaching its potential (AI’s black box, legal culpability, hype/fear among radiologists). Although all of this has been discussed before within the radiology community, these two high-profile stories certainly help get the word out to an even wider audience.
  • The Feinstein Institutes announced the FDA clearance of its Fluorodopa F 18 Injection PET imaging agent for Parkinson’s disease diagnosis and progression tracking. Fluorodopa F 18 helps visualize dopaminergic nerve terminals in the striatum, marked by lower FDOPA uptake in patients with Parkinson’s disease. The agent may also help monitor patients who receive intracerebral transplantation of adrenal medulla tissue or fetal mesencephalic tissue.
  • Visage Imaging signed a cloud-based Visage 7 enterprise imaging deal with teleradiology startup, Nines, worth an estimated $4.2 million over 5 years. Although Nines isn’t nearly as big as the major hospital systems that Visage Imaging signed over the last few years (including: OSU, Duke Health, Partners, Mayo Clinic, Mercy Health, and Yale-New Haven), the startup has some serious buzz and serves as an example of how Visage 7 can scale across different environments.

The Resource Wire

  • In this Nuance video, Penn Medicine professor, Warren B. Gefter, shared how PowerScribe One leverages AI, structured data, and automation to drive improved patient care.
  • The first patients were recently treated in a clinical trial using focused ultrasound to enhance the effectiveness of chemotherapy drugs in those with Her2+ breast cancer that has metastasized to the brain.
  • This Carestream blog highlights the top reasons healthcare providers should make the shift to digital.
  • Yale University research reveals that the average patient drives past six lower-cost providers on the way to an imaging procedure, due in large part to patients’ and physicians’ limited cost consciousness. Medmo helps address this issue by letting patients enter what they can afford for their scan, then booking them at a nearby imaging center willing to accept that rate.

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