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January 11, 2022 News 11 Comments

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Revenue cycle technology and services company R1 RCM will acquire competitor Cloudmed for $4.1 billion.

R1 has made a handful of acquisitions over the last several years, including VisitPay for $300 million, Cerner’s RevWorks business for $30 million, Intermedix for $460 million, and SCI Solutions for $190 million.

The company made a comeback after several widely reported missteps under its former name Accretive Health – settlement payouts for aggressive patient collection tactics and lapses in data security, followed by delisting of its shares from the New York Stock Exchange – and was renamed to R1 RCM in 2017.

RCM shares trade on the Nasdaq, with a market capitalization of $6.5 billion.

CloudMed Solutions – sold to Revint, then named Cloudmed — was founded by Jason Merck, now EVP of Cloudmed, in 2015.

HIStalk Announcements and Requests

I’ve long wished for IOS-type capability in Chrome to be able to send a web page’s link (nearly always to myself) via Gmail, but somehow I never thought to Google a solution until today, when I found an ancient Chrome extension called Send from Gmail (by Google). It hasn’t been updated since 2013, but it seems to work fine, much easier than copying the web address and composing a new email and pasting it in.

Discuss: a physical line of people waiting for something indicates a failure of technology to meet a need.


January 13 (Thursday) 1 ET. “Cultivating gender equity in STEM.” Sponsor: Intelligent Medical Objects. Presenters: Laura Miller, CEO, TempDev; Amanda Heidemann, MD, CMIO, CMIO Services, LLC; Deidra Jackson, VP of IFP customer success, Bright Health; Sunita Tendulkar, VP of agile portfolio management, IMO. Despites strides that are being made, women make up only 27% of the STEM workforce. This panel discussion will cover mentorship, STEM education, pay gaps, and debunking stereotypes.

Previous webinars are on our YouTube channel. Contact Lorre to present your own.

Acquisitions, Funding, Business, and Stock


Clinical communications, collaboration, and scheduling technology company PerfectServe acquires AnesthesiaGo, a developer of automated daily case assignment software for anesthesia staff.


Multi-vertical analytics and data services company Qlik files for an IPO, with date, number of shares, and pricing yet to be determined. Thoma Bravo acquired Qlik in 2016 in a $3 billion deal, taking it private after facing pressure from activist hedge fund Elliott Management.


Healthcare collaboration software vendor TigerConnect secures $300 million in funding from Vista Equity Partners, bringing its total raised to $400 million. Competitors Voalte and Vocera were acquired by medical technology vendors in 2019 and 2022, respectively, for $180 million and $3 billion.


Transcarent, the employer health insurance cost management software company led by Glen Tullman, raises $200 million in a Series C funding round that values the company at a reported $1.6 billion. It has raised $300 million in just over a year. Transcarent inked a deal with Walmart last October to offer the retailer’s pharmacy services to its self-insured employer customers.

OR block time management technology vendor Copient Health raises $3.2 million in a Series A funding round. The co-founder and CEO of the Atlanta-based company is industry long-timer Mike Burke, who previously founded Dialog Medical and Clockwise.MD.

ASC revenue cycle management company National Medical Billing Services acquires MdStrategies, which offers medical coding services to ASCs.

Primary care enablement company Aledade acquires Iris Healthcare, which offers advance care planning solutions. It is the first acquisition for Aledade, which was co-founded in 2014 by former National Coordinator Farzad Mostashari, MD, MSc, who serves as the company’s CEO.


  • Baptist Health (KY) and Prisma Health (SC) select Well Health’s patient communication software.
  • Pullman Regional Hospital (WA) will partner with Providence to replace its Meditech Magic system with Epic by March 2023.
  • Grail will use Premier’s PINC AI clinical decision support technology to better identify patients eligible for its Galleri multi-cancer early detection test.
  • Meditech will integrate SecureLink’s critical vendor access management software with its systems.



Availity hires Bobbi Coluni (IBM Watson Health) as chief product officer.


Niall Brennan (Health Care Cost Institute), a former CMS chief data officer, joins Clarify Health as chief analytics and privacy officer.


Revenue cycle technology vendor MedEvolve hires Branden Barkema, MBA (North Florida Surgeons) as chief revenue cycle officer.


Kerri-Lynn Morris (Microsoft) joins The SSI Group as CTO.

Announcements and Implementations


Wolters Kluwer Health announces GA of its Ovid Synthesis application suite, which includes Clinical Evidence Manager, its first cloud-based workflow management module.

Pivot Point Consulting’s “Healthcare IT Directions Report” highlights four trends for 2022:

  1. Healthcare and health IT will be challenged in unknown ways by job resignations.
  2. Telehealth and remote patient monitoring will cover a wide scope based on patient demand, patient population characteristics, and access enablers / limiters.
  3. Spending on public health infrastructure will ease data access and reporting while creating career opportunities.
  4. Deployment of interoperable EHRs to retail sites – such as Walgreens, CVS, and Walmart rolling out Epic – will allow retailers to compete with traditional healthcare providers, with the latter needing to embrace a digital strategy to offer a frictionless patient experience as a differentiator to offset the convenience of retail healthcare.



Healthcare visionary and cardiac surgeon Devi Shetty, MBBS, MS — chairman and executive director of India-based hospital operator Narayana Health — says that 95% of illnesses will soon be treated via telemedicine since healthcare requires only data, with few patients needing hands-on services such as surgery. He also predicts that EHRs will diagnose conditions better than doctors within five years, and that shortly after, doctors will be required to obtain a second opinion from software before initiating treatment.


A non-profit consumer group publishes Upsolve, a free app that allows consumers to file Chapter 7 bankruptcy – often necessitated in the US by medical debt — without an attorney, paying only a $338 federal court filing fee (which the app also applies to have waived). The company’s mission is to destigmatize bankruptcy for consumers as has already happened with businesses, for which it is just a smart financial strategy to avoid paying debt. The group warns, however, that people can file Chapter 7 only once every eight years, so they should consider when to file if they are undergoing long, expensive cycles of chemotherapy.

Sponsor Updates

  • Meditech will offer Expanse users access to role-based, interactive online training courses from MedPower.
  • Clearwater publishes a new white paper, “Technical Testing and the HIPAA Security Rule: What’s Needed to Protect Your Healthcare Organization.”
  • Appriss names Annie Edwards (Luma) chief people officer of its Bamboo Health and Appriss Retail businesses.
  • Azara Healthcare will host its annual user conference May 2-4 in Boston.
  • Fortified Health Security CEO Dan L. Dodson is elected to the AEHIS Board of Trustees.
  • Delaware’s DHSS Division of Substance Use and Mental Health surpasses a milestone of 100,000 referrals through the Delaware Treatment and Referral Network, which is built on Bamboo Health’s OpenBeds platform.
  • Frost & Sullivan recognizes About as a patient access and orchestration leader with its 2021 Best Practices Customer Value Leadership Award.
  • Divurgent publishes its “2022 IT Trends & Insights Report.”
  • Elsevier adds its most advanced 3D full female model to its Complete Anatomy 3D platform.

The following HIStalk sponsors have achieved top rankings in Black Book Market Research’s latest population health tools and solutions report:

  • Population health AI tools: Olive AI
  • Population health/value-based care consultants: hospitals & health systems: Change Healthcare

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Currently there are "11 comments" on this Article:

  1. RE: Discuss: a physical line of people waiting for something indicates a failure of technology to meet a need.

    Not necessarily. The ‘line of people’ and the ‘something’ in this statement are a generalization and assumption of context. The people may not have nor want a technology based solution that delivers the ‘something’ without participating in a line. If a technology has been applied already and that results in a line, is the line formed because of a new demand for the ‘something’ that would not have otherwise been available without technology? It would be better to restate as a question; Does a physical line of people waiting for something present an opportunity for technology to meet a need?

  2. #America – where there is a need for a service that helps patients who have the audacity to try to not die from cancer; a way to declare bankruptcy.

  3. Re: Physical lines. I would argue that more often than not in current times, the technology is perfectly capable – it’s the process surrounding the technology that has failed

    • Compare US to other counties testing ability, and it’s clear we have failed in US, when the tech is available. Thirty years in healthcare IT implementations has taught me that hospital ABC can implement BestEverSystem and have great outcomes and usage, and hospital XYZ installs same system, but is a colossal failure. Other countries have managed testing quite well, according to family that has experience with it. I’m sad to see the missteps US has taken, but I don’t think it’s intentional, just lack of experience in true public/social health environment. It’s obvious when they say “every health insurer has to provide 8 tests per person per month” that they have no idea what they are doing…insurers should have zero to do with it, in my opinion.

  4. RE: Discuss: a physical line of people waiting for something indicates a failure of technology to meet a need.

    .. unless it is the newest iPhone ???

    • That might be corollary: waiting in line is a feature when people are pleased to do it, like Apple fans happily chattering with fellow line-waiters for their chance to enter the Temple of IOS (the ones that remain open, anyway) even when they could have conducted their business online. I’m also thinking of those barbeque places in Texas where the multi-hour waits are part of the ritual and the restaurant passes out free beer in turning the queue into a pre-experience party. Corollary 2: a line of people waiting for a high-demand product or service creates a bandwagon effect that validates someone’s presence in it as rational and that may create more demand as passersby wonder what the fuss is about (like Epic turning down prospects in its early days in creating FOMO).

  5. Re: Devi Shetty’s predictions.

    My counter-predictions:

    – “95% of illnesses will soon be treated via telemedicine”
    Telemedicine will continue to grow in popularity. 95% is a pipe-dream, and even if you extrapolate “soon” to 5 years, it won’t hit that level. Also, where is your data going to come from?

    – “EHRs will diagnose conditions better than doctors within five years”
    Balderdash! There are some promising results for a tiny handful of problematic clinical outcomes. There have also been some failures (cough *Epic* cough). This is all I foresee happening in this space in that timeframe.

    – “doctors will be required to obtain a second opinion from software”
    Actually, it will be a first opinion, it will be 100% optional, and the clinician will be able to override it. Frequently the software will either be wrong or will offer no diagnosis whatsoever. And the reasons will be either not enough data or not enough certainty for algorithmic resolution. Nevermind the technical, clinical, administrative and institutional hurdles that must be overcome to implement this idea.

    • Thanks Brian Too,

      I couldn’t agree more for all the reasons you said and more.

      The writer assumes that there is data of sufficient quality to make a diagnosis, but of course, if 95% of the encounters are telemedicine that data will come from where? Wearables or IOT? Put the 95% aside for this conversation because it won’t happen.

       Let’s go to the data that would be used for this “second opinion”, that data would be mostly entered by a clinician in the workflow, and that data will be biased, as it is today, by many factors.

      Clinician habits, “it is the dx code I know best so it always goes.” Administrative influence, “I want this dx code because I can upcode my billing.” Payer influence, “I need you to use these dx codes because they are part of my managed care cohort for HEDIS/MIPS/MACRA.” Government influence, “I need you to use these dx codes because my automated case reporting is looking for those codes.”

      So let’s go to workload. “As the clinician who has 15 minutes to see and chart this encounter I have created templates that facilitate documentation.” “As the clinician I don’t have time to search the 68,000 clinical diagnostic ICD-10 codes to give you a precise answer. And, I am so looking forward to the solution to that problem by adding another 20,000 codes for ICD-11.”

      Let’s go to the vendors both primary and after market. “As a third party vendor I have created InteliNotatiaMaximus, (trade mark pending) that gathers all the encounter information and formats it into a encounter note with a plan — and I haven’t revealed the algorithm used so you can’t deterministically validate the INM solution” “As the EHR vendor I document things differently than another EHR vendor, so AI from that vendor pointed at my EHR will give a different result.” “As the EHR vendor I want to keep my secret sauce secret, so you can’t validate my AI against your medicine.” “As the EHR vendor I created my AI to work against a Family Practice, so my AI will work on an Internal Medicine practice, right?” “As the vendor, I hire bright software engineers, who have never practiced medicine, or even know the language/workflow of the department I am coding for — that will work out just fine, right?”

      From the interoperability aspect. “I need to pull data in from other clinical sources, but the variability of that data makes it impossible to reconcile and incorporate into my system. I can read it, but not incorporate it.” “Once I have brought data/information into my system how do I know when it has been updated or changed?”

      For all those reasons, the data that is used to train AI is, challenged at best, and crap at worst. You can get through the note and see what you need to see as a human today, but training up AI from that variation is nowhere near being ready. The last 10 years are rife with AI failures, but we keep thinking that without changing the underlying data and data failure causalities, we will get a different result.

      And lastly, do we really think that there are EHR vendors out there who are ready to deliver a medical device? That are ready for that regulatory rigor? Because, if you start letting the EMR do the primary diagnosis, or even the second opinion, I think you have crossed that line.

      Yes, I want better data, better information, better medicine, better decision making — what do we need to do to get there?

      • I am reminded of an incident I witnessed, caring for my late Mother. We got a test for cancer performed and it came back negative.

        Yet in discussing this with the Attending, I listened in amazement as he kept talking about the possibility of cancer. When I questioned him about this, he said “oh yes, we see this all the time. A negative test result, yet the organ is cancerous!”

        So how exactly does an algorithm cope with this? Simple logic will fail–the test was unambiguously negative. Nor can you assign a simple confidence level of X% to a test such as this. I’d suggest that a positive result would get a high confidence rating, while a negative result would get a low confidence rating.

        I see examples of this type of complexity all over medicine. No matter what sort of technical magic you throw at this, you are facing high barriers to implementation.

        • First, I am sorry for your loss.

          Medicine is complex for all the reasons you and I both mentioned, and as has been called out by many people before us.

          I want an expert looking at my medicine, and that expert is not a Watson, or a (cough cough) algorithm.

          The conversation I had this weekend about medications, medication coding, and medication coding alignment (I won’t go into too many details) demonstrated how bad data is out there, and how bad data leads to bad information — and how that is influencing science’s attempt to achieve knowledge and wisdom.

          We do need to do better, but that requires acknowledging that we aren’t all that and three bags of chips at this point in time.

          Again, sorry for your loss, but I hope that this has driven you to make this field better — it can be, if we only acknowledge its current state and strive to change.

  6. “EHRs will diagnose conditions better than doctors within five years” and “doctors will be required to obtain a second opinion from software”

    Well, to put it more broadly, what Dr. Shetty is saying is that algorithmic assisted diagnosis of conditions will be in general of a higher quality than those done by human beings alone. I don’t think there’s anything controversial or revolutionary about this idea.

    In almost any field involving decision making, it has been shown empirically that due to our cognitive limitations, we end up introducing a lot of bias and noise and that the bias and noise is not random but systemic. I refer you to Michael Lewis’ book The Undoing Project (on Nobel Laureate Daniel Kahneman and his collaborator and friend, Amos Tversky) and Kahneman’s own book – Noise.

    In Undoing Project, the author talks about a study undertaken in the 60s in Oregon (“Man versus Model of Man”), where a simple, rules based algorithm developed based on expert input of Radiologists was better at diagnosing cancer (from stomach X-Rays) than those very Radiologists themselves. Our data quality and algorithm sophistication has only improved since then.

    Algorithm assisted decisions will reduce that systemic bias + noise and will lead to higher quality diagnosis (and do it with better predictive capabilities for more upstream care). Of course, nobody is denying that for this to happen at scale, data quality, transparency in algorithm development process, awareness of clinical applicability etc. will all need to improve and the industry will do well to stay clear of AI/ML snake oil peddlers. Thoughtful visionaries will create and own that future – just like they always have.

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