Bryony Winn

In our recent chat, Christina Farr defined today’s healthcare market in one word: weird. As “stay alive until 2025” shifts to “grow now,” boards and investors are seeking new CEOs to usher in long-term sustainability. But the weird market is complicating things. That’s why I was excited to join Chrissy, Ash Zenooz, MD, and Luba Greenwood, J.D. on the latest episode of the Second Opinion Media podcast. Listen here: https://lnkd.in/edyDpGny We discussed everything companies and candidates need to build the relationships that form successful CEO searches. Here are three key takeaways. 1) The traditional view of what makes the “right” CEO is changing. Boards need to look beyond what CEO candidates have done and where they’ve worked. A first-time CEO who understands your challenges and offers creative solutions might be a better bet than someone who’s been there and done that. 2) CEO success is a give and take. To deliver results, your next leader needs to bring adaptability, reliability, and quick decision making — an experienced executive chair, and patience to build the proper team will pay long term dividends. 3) Relationships beat transactions. Just as transactional relationships hurt healthcare, transactional CEO searches hurt companies. Finding the right leader is about understanding candidates as people: their backgrounds, thinking, and outcomes. Invest the time to build a relationship with your candidates, and do it in person. Thank you for sitting down and talking all things healthcare talent with me, Chrissy, Ash, and Luba! YouTube: https://lnkd.in/edyDpGny Spotify: https://lnkd.in/eWb6p_z2 Apple: https://lnkd.in/eEDTp4VQ

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S&P: Medicare Advantage's growth a bad omen for hospitals - n8: and Many Health Systems Continue to Participate Under the Terms Dictated by the MA plans, Enabling this Abuse “Even beyond rates, MA adds more administrative and liquidity hurdles for providers. S&P pointed to MA plans’ “key strategy” of prior authorization requirements, which force providers to absorb associated refiling costs and sometimes keep patients locked in a longer hospital stay. That burden is exacerbated by insurers’ generally “high” denial rates, S&P wrote, which have led to instances where smaller providers without the resources to fight claim denials terminate their MA plan contracts and become more vulnerable to closure…. The report also notes that many MA plans have longer pay cycles than traditional Medicare. Combined with claims denials and payment uncertainty, the delayed reimbursement “can hurt cash flow and liquidity,” according to the report.” https://lnkd.in/g3-drT-S So it goes n8 #healthsystems #hospitals #valuebasedcare #medicareadvantage #valuebasedscam

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"The [Washington Center for Equitable Growth] report then examines the current evidence for what federal and state policymakers can glean to address these issues, before delving into more detail on the competition issues with which these policymakers must grapple to deal with hospital consolidation. The report then closes with a comprehensive set of suggested policy actions. These policy recommendations include suggested legislative and administrative solutions to increase the transparency of hospitals’ pricing and quality-of-care metrics, as well as proposals to improve antitrust reviews of mergers. There also are suggested ways for decisions by the judiciary to be better informed with better data-driven evidence of the competitive harms caused by hospital consolidation. All these practical solutions to better antitrust enforcement of hospital mergers rest on suggested ways in which policymakers at the federal and state levels can work with the national antitrust laws and regulations as they stand today—recognizing that, for a variety of reasons, antitrust remedies alone cannot resolve the growing problem of the increasing consolidation of hospitals in our nation." Quite a report on #hospital #consolidation. Lot of strong policy suggestions but no silver bullet. https://lnkd.in/dmYQcxZx

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It was a pleasure to talk with Minalkumar Patel about the distinct needs of ACOs and where we see the biggest opportunities to optimize data to benefit patient health and provider success. I am delighted to share our conversation and look forward to much continued discussion on this important area of innovation for our industry. #valuebasedcare #aco #healthtech

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Fantastic Keynote this morning by G.T. Sweeney at the KLAS Research Digital Health Investment Symposium #DHIS. "Wringing out Inefficiencies in Healthcare" Transparency 🔸 "You can't have efficiency in a market without Transparency"   Platform Thinking 🔸 It's slower in healthcare compared to other industries. 🔸 A handful of platforms will emerge 🔸 With platforms the administrative burden of health care will melt away, so we can return our focus to clinical outcomes.   Collaboration 🔸 Find ways to collaborate with each other beyond what we have done in the past. Consolidation 🔸 We continue to see consolidation in health systems and in consumer tools   Value Based Care -"a network of networks" 🔸 There are elements of healthcare that should be addressed nationally (Ex: we should treat diabetes the same everywhere) 🔸 But much of our care is and should be hyper local. Delivery, care,   Customer-centered Care Coordination 🔸 It's still really, really hard. 🔸 Care plans are all different based on the involved stake holders (Payers recommend one thing, doctors another, etc) 🔸 We need a shared care plan in the cloud. 🔸 Show irrefutable facts of patients. (Bios, procedure dates, name, etc.) 🔸 Separate the facts from the recommended clinical actions. 🔸 But then create an eco-system of insights that makes it easier for patients AND clinicians   Cost Containment of subscription based services 🔸 Don’t require and bundle investment in services that are not needed.  

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Aetna plans to automate 30% of its utilization management (UM) requests by the end of the year. What are the potential benefits and drawbacks of this decision? Utilization Management (UM): UM ensures patients receive the right care and in the most cost-effective setting. This involves reviewing requests to determine if they are medically necessary and covered by the plan. Automation in UM involves Augmented Intelligence and machine learning algorithms to process UM requests. These systems analyze patient data, medical guidelines, and historical claims information to make decisions about coverage. 1. Increases Efficiency: Automating UM processes can significantly reduce the time it takes to review and approve requests. AI algorithms can process large volumes of data much faster than humans, leading to quicker decisions. This means patients experience shorter wait times for treatments and procedures, improving patient satisfaction and potentially leading to better health outcomes. 2. Reduces Costs: Automation can reduce administrative costs associated with manual UM process. This leads to savings in overhead expenses. These cost savings can be passed on to consumers through lower premiums or to healthcare providers through higher reimbursement rates. 3. Accuracy and Consistency: Automated systems are less prone to human error and bias. They apply pre-defined rules and algorithms consistently, ensuring all requests are evaluated fairly and objectively. This leads to more accurate decisions about coverage, reducing the likelihood of denials based on subjective interpretations of medical necessity. Possible Cons: 1.Potential Errors and Bias: While designed to be objective, AI algorithms are only as good as the data they are trained on. If the training data reflects existing biases in the healthcare system, the automated system may perpetuate those biases, leading to unfair or inaccurate decisions. This could result in certain demographics with specific conditions being unfairly denied coverage or facing unnecessary delays in treatment. 2. Lack of Human Oversight: Automating UM processes may remove the human element from decision-making. While efficient, this can lead to a lack of understanding in cases with complex medical or social factors.Patients may feel unheard or misunderstood if their cases are handled solely by an algorithm, potentially leading to dissatisfaction and distrust in healthcare systems Automating 30% of UM requests presents opportunities and challenges. While it has the potential to increase efficiency, reduce costs, and improve accuracy, it's crucial to address complex concerns. Aetna should ensure diverse and unbiased training data for their AI algorithms to minimize bias and ensure equitable coverage decisions. Keep human oversight in the process, particularly for complex cases. Invest in retraining and upskilling programs for employees whose roles are impacted by automation, prepare them for the evolving landscape.

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