Today on Health in 2 Point 00, Jess and I are getting in the spirit of things with this week’s Democratic debate. In Episode 87, Jess asks me about Omada Health’s $73 million raise, bringing its total to $200 million, and about what happened with nursing home telehealth startup Call9 shutting down. We turn to politics with Trump telling HHS to have hospitals publish their price list—and it’s unclear that this is even going to make a difference—and to health care coverage in the Democratic debate. —Matthew Holt
DNA testing companies like 23andMe and Ancestry
have made DNA testing mainstream, with adoption skyrocketing among consumers.
Meanwhile, health tech startups like Veritas Genetics are starting to push the trend
even further – from genotyping to whole genome sequencing. What’s the
difference? Well, genotyping looks at less than half of 1% of your
genome, while whole genome sequencing looks at over 99% of your genome.
Veritas is betting that consumers are ready for
what’s revealed by looking at more than 6.4 billion letters of DNA and are
promising that the value of that information will only get richer as time goes
on and the science that makes sense of our genome achieves new breakthroughs.
In fact, Veritas is positioning their $999 test
as “a resource for life” and Rodrigo Martinez, their Chief Marketing &
Design Officer who I chat with here, shares a vision for the future that
includes asking Alexa to scan your genome before taking medications or risking
allergic reactions to foods.
This is fascinating proposition for the future
of health (investors are jazzed too, having poured $50M into the company), but
ethical questions abound. How do you make this information useful and
actionable? How do you handle situations where major health issues are reveled?
And what about data privacy? This is about as personal as personal health
information can get. Rodrigo weighs in…
A hot take on healthcare in the Democratic debate: They’re doing it wrong.
Healthcare is not a reason to choose between the Democratic candidates.
They are all for greater access and in some way to cover everyone, which is great.
None of their plans will become law, but if they are elected those plans will become the starting point of a long discussion and legislative fight. The difference in their plans (between, say, Buttigieg or Biden and Warren or Sanders) is more of an indication of their general attitude toward governance rather than an outline of where we will end up.
Democrats are focused on coverage, Trump is on cost.
Around 90% of Americans already have coverage of some sort. Polls show that healthcare is voters’ #1 priority. Read the polls more closely, and you’ll see that it’s healthcare costspecifically that they are worried about.
Democrats seem to assume that extending more government control will result in lower costs. This is highly debatable, the devil’s in the details, and our past history on this is good but not great.
The President, on the other hand, can make flashy pronouncements and issue Executive Orders that seem intended to bring down costs and might actually. It’s highly questionable whether they will be effective, or effective any time soon. Still, they make good headlines and they especially make for good applause lines at a rally and good talking points on Fox.
But, Ms. and Mr. Average Voter will hear that Trump is very concerned about bringing down their actual costs. The Democratic plans all sound to the untutored ear (which is pretty much everyone but policy wonks like you and me) like they will actually increase costs while taking away the insurance that 90% already have in one way or another.
It is important to take care of everyone. But it is a mistake for the Democrats to allow this to become a battle of perception between cost and coverage. Voters’ real #1 concern is about cost, not coverage.
Joe Flower has 40 years of experience in the healthcare world and has emerged as a thought leader on the deep forces changing the system in the United States and around the world.
Every so often, my cynical self emerges from the dead. Maybe it’s a byproduct of social media, or from following Saurabh Jha, who pontificates about everything from Indian elections to the Brexit fiasco. Regardless, there are times when my attempts at refraining from being opinionated are successful, but there are rare occasions when they are not. Have I earned the right to opine freely about moving on from financial toxicity, anti-vaxers, who has ‘skin in the game’ when it comes to the health care system, the patient & their data, and if we should call patients “consumers”? You’ll have to decide.
I endorse academic publications; they can be stimulating and may delve into more research and are essential if you crave academic recognition. I also enjoy listening to live debates and podcasts, as well as reading, social media rants, but some of the debates and publications are annoying me. I have tried to address some of them in my own podcast series “Outspoken Oncology” as a remedy, but my remedy was no cure. Instead, I find myself typing away these words as a last therapeutic intervention.
Here are my random thoughts on the topics that have been rehashed & restated all over social media outlets (think: Twitter feeds, LinkedIn posts, Pubmed articles, the list goes on), that you will simply find no way out. Disclaimer, these are NOT organized by level of importance but simply based on what struck me over the past week as grossly overstated issues in health care. Forgive my blunt honesty.
● Can we have fewer posts and papers that describe how immoral financialtoxicity is? We all know it’s a problem and our patients suffer the most from it. But continuing to mention the gravity of financial toxicity? Well, that’s just so 1999. At this point, I want more posts and papers discussing strategies on how we move forward. For example: How can we overcome financial toxicity? Even if our patients appreciate us continuing to discuss the same problems repeatedly, they deserve better answers from us.? Let me illustrate. Say I am your patient and I complain to you about persistent nausea. You, as the doctor are empathic and actively listen to my concern, yet my nausea persists. I appreciate the attention and the listening you offer, but at some point I need something to control my nausea. If you don’t have the remedy, I am more annoyed because you keep restating my problem, agonizing me further and still not offering me a solution or showing any attempts to try to find a solution for my issue.
● I am growing tired of the debate on “vaccines”. Isn’t it clear that by now, if there are people who do not believe in vaccines, there is not much we can do to sway them differently? There comes a time when one must decide where to concentrate his/her energy. I am all for having an open dialogue. But, a dialogue with the intent of changing one’s opinion requires both parties to be open to each other’s views and that one of them might potentially change course. Based on what I have seen over the past few months, those opposed to vaccines will not be persuaded by strong evidence or the amount of data they are given. So, maybe we should direct our attention to something that brings better results? Say, describing financial toxicity one more time? OK, that was not nice.
● There are many stakeholders in the health care industry, but the ultimate stakeholder is the patient. Aren’t we all previous patients, current patients, or future patients? I am growing tired of folks pointing fingers at each other as the solely responsible party for the current state of affairs. Academics blame pharma, pharma blames research costs, insurers blame both, patients blame insurers, physicians blame the system, and the list goes on & on. We need to be fair and practical if we are to approach our health care system in a methodical way that lends towards some solutions. The reality is, EVERY entity is important in assuring proper delivery of life-saving drugs to patients who stand to benefit. We all can name hundreds of therapies that were developed outside the walls of academic and university labs, and similarly name many medication that required collaborations between academia and pharma to achieve success. Pharma defends itself from being the culprit, challenging us to envision how our current drug development and research ability is without the manufacturer’s taking risks? Would we have the “Gleevecs” of the world? Likely not. Could these drugs be much cheaper and could we have a more rational approach to drug pricing? Absolutely. But, hospital prices also need a better rationale for the costs of blood draws to x-rays, and the absurd costs of a Tylenol pill in an inpatient ward. Why do academicians rarely critique hospitals? Because they are employed by such hospitals. In general, it isn’t advisable to critique the employer that issues your paycheck. I plea that the critique must be fair, balanced, and equally distributed among all stakeholders.
● Since we all know that a few patients are treated on clinical trials, we need to figure out a way to incorporate data generated from non-trial patients into decision-making. That’s what I call the “real world” Yes, it’s not perfect, but such is life. Less critique to the idea of studying the real-world and more thoughts of how we should analyze such imperfect data would be welcome. If I bet a dollar for every time I see a post contending “we all live in the real world; my world is real; there is no such unreal world”, I would be as rich as Jeff Bezos, before his divorce debacle. Bottom line, we live in the real world, so let’s embrace its imperfections and figure out how we proceed. We can’t answer every question with a randomized controlled trial; that’s just not doable. We can, however, learn from ‘patient Bob’ that encountered a toxicity not mentioned in a clinical trial; knowing that such toxicity can be seen in the real-world might help manage subsequent patients like ‘Bob’. For example, if we were to apply the aforementioned case to the real-world, when the initial study on Ibrutinib in CLL was published in NEJM, it did not report atrial fibrillation as a potential toxicity. However, now no CLL treater or a hematologist would dispute atrial fibrillation as a potential adverse event. I credit real-world data with this piece of information. Let’s utilize ALL of our resources symphonically to optimize patient care. That should be our guiding principle.
● I see many complain when patients are labeled as “consumers” and when doctors are called “providers”. The sense is that these definitions demean both. I can understand this viewpoint, but is this really a problem that is worth spending time on debating? Have we really resolved all health care issues such that we are now simply arguing whether we call ourselves providers or physicians? Wouldn’t that be luxurious? If labeling patients as “customers” or “consumers” of the health care system will force the system to accommodate patient’s needs, I am all for it. Why not? Whatever it takes to decrease wait times, improve satisfaction, and allow patients to enjoy the experience despite having an illness. If we view patients as “consumers” of what we have to offer, and recognize that consumers in any market have choices, maybe we would be incentivized to improve the subtle comforts in our health care delivery model. If the end goal is to maximize the patient experience, then let’s not get hooked on how we label this and that. As doctors, we “provide” healthcare service, expertise, help, listening ear, etc etc. Like it or not, we are “providers of health care”. Let’s refocus the debate on what best serves our patients and take a critical look at more pressing topics than nomenclature.
I am sure that every reader has his/her own laundry list like mine and the list changes based on whether the Patriots won, or if your coffee was made with cream or diluted almond milk. I shared some of my nuanced thoughts with you because I believe we have bigger problems to solve. We need action plans to help serve patients better, move the needle from talking about financial toxicity to solving it for the sick and vulnerable, and (yes, I mean everyone here) needs to collaborate and try to align our interest in recognizing that patients are the ultimate end user of the health care system. Thanks for indulging me as it was quite cathartic, and I might lobby to have a new laundry list of complaints every month (until I get blocked by the editor)!
Chadi Nabhan is an oncologist in Chicago. His interests include strategy and business of healthcare. He’s a prolific speaker and occasional tweeter. He can be reached @chadinabhan
The year is 2019 and Imaging By Machines have fulfilled their prophesy and control all Radiology Departments, making their organic predecessors obsolete.
One such lost soul tries to decide how he might reprovision the diagnostic equipment he has set up on his narrow boat on the Manchester Ship Canal, musing at the extent of the digital take over during his supper (cod of course).
What I seek to do in this short paper is not to revisit the well-trodden road of what Artificial Intelligence, deep learning, machine learning or natural language processing might be, the data-science that underpins them nor limit myself to what specific products or algorithms are currently available or pending. Instead I look to share my views on what and where in the patient journey I perceive there may be uses for “AI” in the pathway.
For the purposes of this discussion I therefore refer neither to “Artificial” intelligence nor “Augmented” intelligence but have instead coined the term “Applied” intelligence as a moniker I feel more fitting for the broad brush.
Whilst I write primarily from a UK/NHS perspective here, I would suggest many of the challenges and potential use-cases presented may be applicable to other systems. Similarly, some of the broad suggestions (for example “clinical decision support”) may be relevant in slightly different guises at different parts of the pathway.
A Global Solution
The NHS is not alone in operating under the pressor of a relentless increase in demand for imaging diagnostics – far outstripping capacity even when upskilling and skill-mix approaches are taken into consideration – by approximately 10% year on year.
At the same time, provision has transitioned from a single local-hospital based general radiology department meeting all the needs of its populace to multiple sites: be it increased community-based imaging resources or federated specialised centres across a region.
The role of the radiologist has also evolved over the past decade or so, driven in part by the increased importance of MDTs (Tumour Boards) but also in a change in Clinical Practice with more explicit shared decision-making.
Thus, one might consider the main challenges to overcome be represented by seeking to overcome issues with:
Increasingly varied demands on a radiologist’s time pulling them away from report-churn
Between different Institutions in an Enterprise/regional care system (we will include home reporting etc in this domain)
Between different departments or specialisms in the same org
I have sought to break the use cases down against 4 broad stages of the patient journey:
i. Clinical Decision Support – tools at the requesting stage which may guide clinicians to the appropriate single best test or suite of tests for a given presentation or differential
ii. Optimised Scheduling – both within an Enterprise and with patients to route appointments to the most convenience and efficient location and scanner to enhance productivity
iii. Enhanced Digital Communication with patients (including Electronic Consent) – tools to better prepare a patient with information about what a test involves, how to prepare for it and the importance of this.
The summated benefit of these measures would be to eliminate time wasted by poor scanner scheduling, reduce the incidence of no-shows, and pump-prime the information a patient needs for a scan to when they are more receptive rather than during the stressful period of attendance as well possibly as reduced time and support-needs during the scan (for example expectations for positioning etc.
1) Image Acquisition Stage
i. AI-assisted image acquisition to reduce the time take to scan (for example multi-parametric MRI scans) and reduce the number of poor-quality images thus potentially also improving accuracy and need for recalls
ii. AI-assisted Dose Management – at a macro-level by reducing signal noise to improve image quality of lower dose scans, and at a patient level
iii. Real-time on-scanner Image detection/analysis. This itself could have a number of potential benefits. The vast majority of scans reviewed by radiologists are undertaken “cold” that is when the patient is no longer in the radiology department or usually not even in the hospital (outpatient scanning). Benefits of on-modality analysis may allow stratification for example:
Critical finding that requires immediate/urgent medical attention
Abnormalities that require urgent/expedited reporting
Normal scan – automated reporting of normal examinations for near-contemporaneous feedback to expedite management and earlier reassurance to patients
A subset of the above might even perhaps be detection of changes to known pathology (for example a nodule or cancer follow-up) with either automation of “no change” or prioritisation of “significant change” findings.
1) Image Interpretation and Reporting
i. Examination-Routing: intelligence worklist management to ensure that examinations are reviewed as quickly and efficiency as possible by the post appropriate person based on rules such as:
Key Performance Indicators/metrics
“Normal” pathways as alluded to above
ii. Optimised Presentation of Imaging – ready for reporting: beyond the bane of radiologist’s life that is “hanging protocols” and “relevant priors” more broadly this would be bringing appropriate investigations, clinical information and findings outside radiology to the reporter’s attention to enhance quality and reduce time wasted from multi-source hunting.
iii. Lesion Segmentation and tracking – yes I recognise there are eleventy billion algorithms in the wild or in development that profess to do this, but instead of “App stores” requiring human intervention to pull individual pieces of software to run and then needing user input to validate each nodule, options could include (but not limited to):
Baked into a natural workflow which (for example) automatically segments out lesions (across the ENTIRE image acquisition not just in individual body part models), measures them, detects changes in prior lesions and presents them as a summarised finding in the report.
On-demand Analysis Aid: humans are generally poor at differentiating between true +ve and false +ve and so Algorithm segments “nodules” presented to validate might lead to over-calls. Instead an interactive tool might be activated on demand to provide a “second opinion” on a region of uncertainty instead of pre-marking multiple regions for a person to accept or reject
iv. Image Analysis Support– this might involve, for example, access to image libraries with suggestions of possible diagnosis of appearances based on pathognomonic features
More specifically this might involve Radionomicsfeatures to help classify tumours.
Another example might include an analysis of the attenuation, enhancement characteristics or MR-signal profiles and suggesting the most likely aetiology based on these parameters.
Of course, we should also remember the more prosaic analysis of pathology on plain x-rays (fractures, pneumothoraces etc etc).
v. Natural Language Processing applications might be employed in various guises such as:
Improve the accuracy of voice recognition while reporting and correct typographical errors whilst reporting or deploy suggested-next methodologies to make reporting more efficient.
Automatic generation of report summary based on the body of the text, including details such as auto-inserting TNM stage based on descriptors of pathology.
vi. Report-Creation – the next step from assisted reporting would be independent report creation modules. We are already seeing some of these in development in the Breast Radiology space but possibilities include:
Breast Second Reader applications – helping to address the massive shortage of radiologists and yet with their requirement to double-report mammograms
Full Template Reporting – as we discussed in the image acquisition phase, if the analysis deems an examination is normal there is no reason this could not generate an appropriate report thus potentially massively reducing the reporting burden of the normals. Indeed, this could equally work with (for example) xrays for fractures – coupled with appropriate routing of the reports.
vii. Clinical Decision Support – access to latest pathways and protocols to ensure radiologist advice conforms to current standards (for example for lesion/nodule follow-up guidelines)
1) Post-Reporting Pathways
This would involve various facets of automatic or optimising routings of the report of its findings such as:
i. Automatic notification to responsible clinicians of critical findings
ii. Automatically scheduling a case to be discussed at the next appropriate MDT
iii. Scheduling/requesting appropriate onward examinations based on the examination findings such as PET-CT or interval CT for nodules as per guidelines
The aim of the radiological journey with applied intelligence is that it should result in greater efficiency in the end to end pathway without increasing the administrative burden on users to deploy it. The net result would be faster and more efficient patient-centric imaging. By considering some of the fully automated outcomes for example for normal imaging we could also seek to redress the massive differential between imaging demand and capacity.
Of course, no Applied Intelligence pathway should be deployed without being rigorously tested and validated – much like any new system deployed in the health: human or digital.
Dr. Malik is a consultant radiologist at Royal Bolton NHS Foundation Trust, where he is Trust PACS and Imaging Lead, Associate CCIO and Divisional Clinical Governance Lead. This article originally appeared on South Manchester Radiology here.
Today on Health in 2 Point 00, Jess and I are back from Europe and there is a LOT going on in health tech right now. In Episode 86, Jess asks me about United Health’s big moves, between acquiring PatientsLikeMe and their acquisition of DaVita Medical going through; integrated mental health company Quartet Health raising $60 million; Xealth closing a $14 million round (maybe now they’ll make Epic relevant); Collective Health’s $205 million raise led by SoftBank,; Vida’s $30 million round led by Teladoc (who knows why Teladoc didn’t just acquire Vida); European telehealth company Zava raising $32 million; and finally, Phreesia going IPO (wasn’t Livongo the one to watch?). —Matthew Holt
We begin by commending HHS, CMS, and ONC for skillfully addressing the pro-competitive and innovative essentials in crafting this Rule and the related materials. However, regulatory capture threatens to derail effective implementation of the rule unless HHS takes further action on the standards.
Regulatory capture in Wikipedia begins:
“Regulatory capture is a form of government failure which occurs when a regulatory agency, created to act in the public interest, instead advances the commercial or political concerns of special interest groups that dominate the industry or sector it is charged with regulating. When regulatory capture occurs, the interests of firms, organizations, or political groups are prioritized over the interests of the public, leading to a net loss for society. Government agencies suffering regulatory capture are called “captured agencies.” (end of Wikipedia quotation.)
The extent to which HHS has allowed itself to be influenced by special interests is not the subject of this comment. This comment is just about how HHS and the Federal Health Architecture can act to more effectively implement the sense of Congress in the 21st Century Cures Act.
Over a decade after establishing the goal of a nationwide health information network, incumbent information brokers, primarily large private-sector hospitals that have consolidated their dominance with over $35 B of Federal incentives, continue to find reasons for delay in transparency and opening to meaningful competition. Standards dominate pretty much all of the proposed ONC Rule as well as companion rules from CMS, and TEFCA. Regulatory capture by the interests of consolidated hospitals and their consolidated software vendors hampers progress on patient matching, patient consent, accounting for disclosures, price transparency, and longitudinal health records. Other lobbyists, including an army of hidden data aggregators and brokers from inside and outside the healthcare industry, although they do not participate directly in the standards process, exert a large influence on obscuring the uses of personal information.
Regulatory capture drives negative progress. At a time when privacy is driving much of the conversation on general data, Congress is being lobbied to weaken the privacy protections on behavioral health data. At a time when opt-in, automated, and transparent financial transactions are ubiquitous, the proposed Rule and TEFCA still avoid opt-in consent models and transparent accounting for disclosures for all uses of personal health data. Computer science has long recognized that re-identification and anonymization are wholly ineffective, and can’t prevent hidden data brokers and machine learning from re-identifying personal health data. Turn-of-the-century health regulations still allow for discrimination and unintended consequences of data use.
The proposed Rule does not adequately account for regulatory capture of the standards that matter for competition. This puts the outcome sought by the 21st Century Cures Act at significant risk. It is understandable that regulators are reluctant to lead innovation in technological standards. But it is notable that neither the patients nor the physicians currently have market power over health information technology. And privacy NGOs representing the public’s rights, 501c3 human rights organizations that defend patients’ rights, have no market power.
In the absence of market power to drive innovation, the role of Government as a payer must come to the fore in standards development and deployment.
Government already pays directly for about half of all healthcare services and indirectly for much of the other half. Yet government involvement in technical standards for scaling patient consent (that would also fix the patient matching problem), accounting for disclosures, price transparency, and longitudinal health records is almost non-existent—yet none of the proposed standards to serve taxpayers have been implemented. Blue Button 2.0 is an admirable initiative but it is has not been adopted for patient-controlled standards such as User Managed Access. The VA and DoD, although they have immense leverage over their private-sector EHR supplier, have done nothing to lead in standards development in support of veterans’ needs for longitudinal health record initiatives and privacy. The work they have commissioned with MITRE has been timid and totally inadequate to the scope of the problem.
History has shown that the proposed ONC and CMS rules will be nullified by regulatory capture. The only way to create a transparent market that supports innovation and cost-containment through competition is for Government, as the primary payer, to take a leadership role in standards development and to deploy standards for the real payers: taxpayers, who need patient-directed interoperability at scale. This can start with Dynamic Client Registration and User Managed Access in all Federal Health Architecture projects and must demonstrate the meaning of “without special effort” for physicians and patients.
Adrian Gropper, MD, is the CTO of Patient Privacy Rights, a national organization representing 10.3 million patients and among the foremost open data advocates in the country.
Deborah C. Peel, MD, is the Founder and President of Patient Privacy Rights.