Technology background red and blue color, circuit board and code. 3d Illustration

A lack of adequate staff training and communication is disrupting the way the VA is able to productively use its variety of health information exchanges.

August 11, 2020 – Editor’s Note 8/17/2020: This article has been updated with a statement from DirectTrust.

A recent report from the Office of Inspector General (OIG) found training challenges, the need for increased community partners, the use of community coordinators, and technology issues that need to be addressed to enhance the Department of Veterans Affairs’ (VA) ability to effectively utilize its health information exchanges and the ability to exchange patient data.

However, OIG found respondents who utilized either VA Exchange or VA Direct cited successes and said VA goals were more attainable with more community participation.

Patient data exchange and interoperability is crucial for the VA to enhance patient care. Not only does the Veterans Health Information Exchange (VHIE) program use the VA Exchange and VA Direct, which is directly connected to DirectTrust, but it also has community partnerships to promote patient data exchange and accessibility.

OIG interviewed and surveyed faculty at the 48 Level 2 and 3 Veterans Health Administration (VHA) facilities. The group also interviewed the VHIE Program Office, while meeting with the Office of Information Technology, Office of Community Care, Office of Rural Health, Cerner Corporation, and two unnamed state HIEs to gain a variety of differing perspectives.

READ MORE: OIG Finds Major Patient Safety Issues with VA EHR Implementation

Based on the VHIE Program Office, 140 VA facilities have access to VA Exchange and VA Direct, but only 28 have implemented VA Direct. Facilities that did not have VA Direct access were either not adequately trained by DirectTrust, did not have community partners that used DirectTrust or were using other HIE options.

“Expansion of VA Direct usage to all facilities would increase the instances of health information sharing and improve the timeliness of health information exchange while efforts continue with development of community partnerships through VA Exchange,” wrote OIG.

“The VA OIG report provides valuable insight and recommendations on how to enhance the Veterans Health Information Exchange program.”

“DirectTrust is a volunteer-driven membership organization and standards body serving as custodian of the Direct Standard, the foundation of Direct Secure Messaging. As such, DirectTrust is not set up to provide end-user training. Typically, vendors provide training on how to use Direct within their platform, as Direct Secure Messaging is implemented differently across vendor platforms.  We’re pleased to report that the DirectTrust EHR Roundtable, in which the VA participates, recognizes the variability in utilization across vendors, and is creating ‘best practices’ guidelines to advance the usability and utilization of Direct Secure Messaging.”

“The VA provides a critical service to our veterans, and coordination of care with community partners is extremely important. DirectTrust values our partnership with the VA, and we look forward to continued expansion and improvement of health information exchange and interoperability throughout all of their facilities and with their community partners.”

READ MORE: VA to Resume EHR Modernization Deployment in October Amid Concerns

But according to additional survey responses and interviews from the 48 VA facilities, OIG found 46 facilities use either VA Exchange or VA Direct, while two facilities do not use either. And of those 48 facilities, 22 said they exchange patient data by mail, fax machine, or scanner.

Respondents noted additional training, an increase in community partners, and an overall better understanding of how to use the health information exchanges as common ways to defeat these HIE challenges.

“In addition, facilities reported technology challenges to viewing community health information through VA Exchange, including the dual sign-on requirement for VHA providers to first sign into the electronic health record and then sign into the Joint Legacy Viewer (JLV) to access community partner patient information,” wrote OIG.

“The JLV data quality was not ideal, information naming and access was not user friendly, and facilities reported a cumbersome process that resulted in delays in finding needed information.”

Currently, VA has two separate contracts that establish community coordination for VHIE, and OIG found 56 community coordinators who work to enhance infrastructure, outreach, and training.

READ MORE: VA Implements Platform to Increase EHR Interoperability for Veterans

But while there are 56 community coordinators, the organization found a varied level of coordinator engagement from high to little or no participation. Respondents also noted that once a coordinator leaves her position, a lack of communication and training issues typically occurs.

“With the addition of more training, communication, and future planned technological changes, VHA could more effectively streamline the continuity of care received by veterans,” OIG wrote.

“Electronic Health Records Modernization should alleviate some of the technology challenges currently experienced with the use of VHIE,” OIG continued. “Cerner reported the implementation of Millennium/Power Chart would eliminate the need for dual sign-in to review community care documents and allow for exchange accesses between VHA, the Department of Defense, and community providers.”

In late April, OIG found major patient safety issues and EHR capability problems centered around the new Electronic Health Record Modernization (EHRM) system and a recent POLITICO report stated VA leaders have acknowledged those issues.

“The OIG made four recommendations to the Under Secretary for Health related to the need for increased utilization of VA Direct, education for staff and veterans on VA Exchange and VA Direct, expansion of community partnerships, and use of contract VHIE community coordinators,” wrote OIG.

OIG said it would follow up with VA until the recommendations are completed or acknowledged.

Article link: https://ehrintelligence.com/news/oig-finds-number-of-issues-with-va-health-information-exchanges

The Joint Common Foundation aims to help the Department to standardize and secure its data and make it easier to find.

The “factory” that pumps out AI tools for the Pentagon is about to get a new tool of its own, one that leaders of the Joint Artificial Intelligence Center, or JAIC, hope will streamline their production and boost output.

The JAIC has awarded a $100 million contract to Deloitte Consulting to create the Joint Common Foundation, or JCF — basically, a tool to help organize the factory, secure it against intruders, direct its workers, and test its products.

. “The Joint Common Foundation will provide an AI development environment to test, validate, and field AI capabilities at scale across the Department of Defense,” the Center said in a statement.

Moreover, the JCF is supposed to help meld various existing programming setups and ease the tasks of building tools that can work across multiple service branches.

“What we currently have is a bunch of disparate AI and ML development environments, duplicative and siloed. A lot of these products in these environments are not interoperable,”  said Col. Sang Han, the Center’s infrastructure chief.

The Joint Common Foundation will also catalogue all the data that the Defense Department has that a programmer might need to train an AI system on.

“We currently don’t have a central marketplace. We don’t have a central…repository or catalogue. That’s what we’re going to do so developers can easily search for data, easily search for AI source code, AI and ML [machine learning] models and products in the DoD,” said Han. The goal is to create a place where “developers can easily come in, pick out the part that they need to build their high-performance application.”

The data catalogue is particularly important. It produces more data than a Forture 500 business and does do in a dizzying array of different formats, from video and voice feeds to images to spreadsheets and text, all classified at different levels. But the center exists, in part, to make sure that AI innovations that occur in one part of DoD can be used elsewhere (so long as that use is ethically justified). Some of that data is what Don Bitner, strategy chief for the JAIC’s Joint Common Foundation and Infrastructure Team, describes as “value-add” data. “We’ve done something to it, we’ve ingested it. We’ve probably paired it down,” Bitner said.

The JCF allows the Department to take that data and put it into a place where anyone–with approval–can look it up and find it. “Being able to do that type of work and have standardized training sets to at least let components and services start the work of natural language processing in a chat room, elevates a lot of the upfront work in presenting data.”

They say that will allow the center to scale up the delivery of new AI tools to services.

Article link:https://www.defenseone.com/technology/2020/08/pentagons-ai-factory-gets-powerful-new-tool/167708/

Do companies still need to hire a large contingent of data scientists to build machine learning models or can AutoML reduce the demand for this elusive talent?

In recent years, as the promise of artificial intelligence (AI) crystallized across industries, organizations revamped their talent strategies to gain the skills necessary to deploy and scale AI systems. They hired legions of data scientists and other data experts to build AI applications, trained analytics translators to connect the business and technical realms, and upskilled frontline staff to use AI applications effectively.

One role in particular, the data scientist, has been especially difficult for leaders to fill as competition for its illusive knowledge increased. Last year, employment-related search engine Indeed.com reported that job postings on its site for data scientists had more than tripled since December 2013. McKinsey Global Institute research has also highlighted the talent shortage and the potential for hundreds of thousands of positions to go unfilled.

Incumbent companies found it especially hard to compete with start-ups and tech giants such as Google to attract or retain the best practicing data scientists and the newest crop of graduates. One multinational retail conglomerate, for example, put in place a highly attractive package last year, with education perks and salaries up to 20 percent higher than market rates, to attract the 30-plus data scientists it needed to support its strategic road map of priority AI use cases.

Certainly, some of this competition may soften as tech start-ups struggle to survive in the wake of the COVID-19 crisis, making it somewhat easier for incumbents to acquire these hard-to-get skills. But there are also new tools that have the potential to fill the data-science talent gap and increase the efficiency of analytics teams. Automated machine learning (ML) tools, commonly called AutoML, are designed to automate many steps in developing machine learning models. Business experts armed with AutoML can build some types of models that once would have needed a trained data scientist.

As one might imagine, there’s a great deal of discussion around what can or should be automated when it comes to model development. However, one thing is clear: the evolution of AutoML tools is driving a radically new way of thinking about data science, expanding its bench to include business experts with extensive domain knowledge, basic data-science skills or the willingness to learn them, and AutoML training, rather than solely filling the team with experienced data scientists.

To stay competitive, we believe companies will be best served by not putting all their resources into the fight for sparse technical talent, but instead focusing at least part of their attention on building up their troop of AutoML practitioners, who will become a substantial proportion of the talent pool for the next decade.

How AutoML tools change the data-science game

To understand this shift in AI talent needs, it’s helpful to grasp at a high level how models—the basic building blocks of AI systems—are created and where data scientists spend most of their time (exhibit).

There are typically six broad steps in the model-development workflow:

1. Understanding the business challenge and translating it into a mathematical one. This is arguably one of the most crucial steps, as the decisions data scientists make here (for example, how they account for the interplay of pricing and demand in an AI-driven price-optimization system) can determine the performance and ultimate success of the model.
2. Understanding the data, including assessing what data are available to support the business goal and the feasibility of leveraging that data to fuel an effective analytical model for the job.
3. Preparing the data, including cleansing the data and identifying the most important features. For example, average operating temperature of equipment and time between maintenance would be key features for helping to predict when maintenance is needed.
4. Developing the models using programming languages such as R and Python by either leveraging one of the many readily available algorithms on open-source platforms or, in much rarer instances, developing a new tailored approach for the problem at hand.
5. Testing and fine-tuning models for performance in meeting the original business goals as well as to address any risks, such as bias, fairness, production readiness, and so on.
6. Deploying the new models into production, embedding them into business and decision-making workflows, and monitoring their performance, making updates as needed.

Many organizations have found that 60 to 80 percent of a data scientist’s time is spent preparing the data for modeling. Once the initial model is built, only a fraction of his or her time—4 percent, according to some analyses—is spent on testing and tuning code. In essence, tuning model parameters has become a commodity, and performance is driven by data selection and preparation.

The field of AutoML aims to automate all data preparation, as well as modeling and tuning steps, so that manual technical work is no longer required. While these tools don’t automate everything yet, they are currently able to produce machine learning models that perform well enough to deliver returns. In the telecom industry, for example, some companies have successfully leveraged AutoML to build profitable churn-management models that predict with sufficient accuracy which customers have a high risk of canceling their contracts.

It’s important to note here that the push to eliminate manual data tasks isn’t new. Today, most machine learning models, including powerful ones such as deep learning, are already fully integrated into programming languages, meaning that data scientists can apply these techniques using very little code. For example, one energy company was able, once it had prepared the data, to build a model that accurately predicted customer cancellations by applying just one line of code. An active and growing open-source community also provides “snippets” of code that data scientists can copy and paste into their models to make the data-preparation and modeling part of their work easier than ever.

It is unclear how far AutoML capabilities will go in automating modeling tasks; complete automation still seems far away. However, it seems certain that these capabilities will make data science ever more accessible to business experts, and, in some cases, business-domain understanding will enrich the quality of many models more than the technical skills of a data scientist.

We already see the transition happening: state-of-the-art tools are enabling AutoML practitioners to build reasonably-high-performing ML pipelines that include all steps—from reading the data to tuning the parameters—without substantial knowledge of machine learning or statistics. One North American retailer, for example, retrained several hundred employees in its business-intelligence team to use an off-the-shelf AutoML platform to perform customer-segmentation tasks that were previously carried out by highly trained data scientists. The move has enabled the company to fill the talent gap between basic business-intelligence functions and very complex ML modeling tasks and save hundreds of thousands of dollars in data preparation.

Certainly, not all data-science challenges can be solved using AutoML tools. At present, the technology is best suited to streamlining the development of common forecasting tasks, where the goal is to predict an outcome, given a few metrics, and the use of black-box models is permissible. Models that require statistical expertise to ensure fairness or build trust—for example, customer-engagement models that help salespeople understand what a prospect is likely to buy and why—still require the expertise of trained data scientists.

The impact on hiring strategies

Given the current limitations of AutoML tools, we don’t foresee demand for substantial, functional data-science expertise going away anytime soon. Over the long term, purely technical data scientists will still be needed, but simply far fewer than most currently predict. We estimate that over the next five years, demand for AutoML practitioners is likely to be twice as high as demand for data scientists as companies build out their talent strategies with both levels of expertise:

• AutoML practitioners, such as biochemists in pharma research, will be able to perform simpler data-science tasks.
• Data scientists with the statistical expertise to understand which tasks can safely be automated without risk will perform highly specialized tasks that can’t be automated, such as developing new algorithms or optimizing accuracy down to the last few percentage points.

How to get started

Where should organizations begin to rethink their data-science talent needs? We recommend companies take the following steps.

Reassess your requirements

The distinction between tasks that can be left to AutoML practitioners and those that require data scientists with deep statistical expertise is not trivial. It requires experienced analytics practitioners to take stock of all the initiatives on the AI road map and triage them based on the complexity of the data and modeling techniques and the necessary level of predictive accuracy. We find the following questions can serve as a useful guide to determine how to divvy up the work on any given task:

• Is this a nonstandard data-science task as opposed to a standard predictive task, such as classification or regression?
• Will we need to use rich and complex data to solve the business problem?
• Is there potential bias in the data, such as in the case of a resume-screening model that may unintentionally reflect historical prejudices?
• Will the problem likely require deeper understanding of statistical methods, such as causal inference?
• Would a slight difference in model performance (for example, a 1 to 2 percent bump in predictive accuracy) significantly influence the value of the model?

To handle tasks for which the answer to any of these questions is “yes,” the organization will most certainly need highly trained data scientists in its talent mix.

Upskill domain experts

The best way to get started with AutoML tools is to train your existing business experts, as opposed to recruiting new hires. Training should include education both in using AutoML tools and in the fundamentals of data science. For example, the business experts should be aware of how common modeling techniques work, what form of data (numeric or text fields) they require, and what patterns the data can (and cannot) reveal. To build out its AutoML team, a manufacturing company piloted a capability-building program for approximately 200 process engineers and line managers. The program consisted of five training days with exercises along the entire use-case life cycle, including standard coding tasks such as cleaning the data and running automated standard ML models, followed by on-the-job coaching when they applied their new skills on their own projects. While education in a technical field such as engineering, physics, or mathematics was a plus, the only prerequisite for these business experts was an interest in and curiosity about data science.

Discuss the limitations—and the opportunities

As highlighted, there are clearly limitations to AutoML technology and numerous pitfalls for companies that use it inappropriately—not least of which are the potential for faulty outputs when it’s used outside its realm of expertise, undetected biases, and lack of explainability. It’s these dangers that have led to concerns in the data-science community. However, organizations that are mindful of the issues and engage in open discussions with their data scientists about the potential of AutoML will not only be able to better deal with current talent gaps but also free up their data scientists for the tasks that really interest them. At the manufacturing company mentioned earlier, data scientists were happy they no longer needed to run every standardized task in the local plants and instead could focus on the tasks that really required their deep and specialized knowledge.

The time is ripe for companies to adjust their talent strategy to take advantage of AutoML tools. These tools enable business experts to efficiently and cost-effectively complete many of today’s simpler data-science tasks and will be even more important in the future as they improve. At the same time, expert data scientists will be freed up for the technically most challenging tasks, enabling them to use their skill set more efficiently and innovate faster, while increasing their job satisfaction—benefits for both the data scientists and the companies that seek to maximize their outputs and retention.

The heads of the U.S. military branches are calling on the Defense Department to stop the transfer of all medical facilities to the Defense Health Agency, saying the novel coronavirus pandemic has shown that the plan to convey the services’ hospitals and clinics to the agency is “not viable.”

In a memo sent to Defense Secretary Mark Esper on Aug. 5, the secretaries of the Army, Navy and Air Force, along with the branch chiefs of the Army, Navy, Air Force, Marine Corps and Space Force, called for the return of all military hospitals and clinics already transferred to the DHA and suspension of any planned moves of personnel or resources.

They said that the COVID-19 outbreak has demonstrated that the reform, which was proposed by Congress in the fiscal 2017 National Defense Authorization Act, “introduces barriers, creates unnecessary complexity and increases inefficiency and cost.”

“The proposed DHA end-state represents unsustainable growth with a disparate intermediate structure that hinders coordination of service medical response to contingencies such as a pandemic,” they wrote in the memo, first obtained by a reporter for Synopsis, a Capitol Hill newsletter that focuses on military and veterans health care.

The DoD launched major reforms of its health system in 2013 with the creation of the Defense Health Agency, an organization initially established to improve the quality of health care available to military personnel and family members and reduce services such as administration, IT, logistics and training that existed in triplicate across the three service medical commands.

But the initiatives ballooned in 2016, with Congress passing legislation that placed the DHA in charge of military hospitals and clinics worldwide, as well as research and development, public health agencies, medical logistics and other operations run by the service medical commands.

On Oct. 1, 2019, all military hospitals and clinics in the continental United States were transferred to the DHA, with those overseas expected to move over by October 2021.

But in December, Army Secretary Ryan McCarthy asked for a temporary halt of the transfers of Army facilities and requested that the Army Public Health Center and Army Medical Research and Development Command remain permanently under the service’s control.

Ryan said he had concerns with what he viewed as a “lack of performance and planning with respect to the transition” by the DHA and Defense Department Health Affairs, according to a memo he sent Deputy Defense Secretary David Norquist.

McCarthy’s comments were the first public statements by a military service in opposition to the transformation, which also calls for cutting roughly 18,000 military medical personnel.

In early March, the Air Force and Army surgeons general weighed in, telling the House Appropriations defense subcommittee that the reorganization is an “extremely difficult” and “complicated merger of four cultures.” They suggested that the Defense Health Agency isn’t ready for some of the coming changes.

The DHA assumed management of all domestic military treatment facilities without the staff or management capabilities to actually run them. As part of the plan, the services were to provide support and guidance for the DHA to run the hospitals and clinics in the interim, until its personnel were ready to operate them.

But then the pandemic struck. And according to a source familiar with operations at several medical treatment facilities in the Washington, D.C., region, tensions that had been bubbling since the initial facility transfer erupted.

At one facility, commanders and DHA leadership argued over who was responsible for the COVID-19 screening tents in the parking lot.

“There are definitely turf battles going on,” said the source, a DoD civilian employee. “[The services] are making it very hard.”

The COVID-19 pandemic has delayed several elements of the military health system reform effort. In March, the DoD placed a 60-day hold on a step to establish administrative markets responsible for military treatment facilities in five regions in the U.S.

In April, the department paused the rollout of its Military Health Systems Genesis electronic medical records program to several new medical facilities, although it continued to modernize the IT infrastructure needed to support the system.

And in June, the Pentagon’s top health official announced that the DoD would delay some of the changes planned for this year, including an effort to begin closing or restructuring 48 hospitals and clinics and sending at least 200,000 patients to private care.

But Assistant Secretary of Defense for Health Affairs Thomas McCaffery, a former health industry executive who took office last August, has said he remains committed to reform, which he believes will improve quality of care while also saving taxpayer dollars.

“There’s been at least 12 times since World War II where there has been efforts to change our system,” McCaffery said during a visit to military health facilities in Washington last week. “All focused on the best way to organize and manage for the mission, have a ready medical force and a medically ready force. The mission is still the same, and having a more integrated system is the way to do it.”

In their letter to Esper, the service heads said the DHA has been helpful during the pandemic in developing standardized clinical practices for the coronavirus response.

But they still asked him to suspend any transfer activity and appoint a working group to explore different options for management of the hospitals.

They also asked that all military hospitals, including two that have operated under the DHA and the National Capital Region since 2013 — Walter Reed National Military Medical Center in Maryland and Fort Belvoir Community Hospital — be returned to their respective services.

They did not say which service Walter Reed would fall under; the medical center was created after a merger between the Army’s Walter Reed Medical Center in Washington, D.C., and the Navy’s National Naval Medical Center in Bethesda, Maryland. It remains housed at Bethesda, a Navy installation.

“We look forward to working together to achieve successful reform of the military health system,” they wrote.

Lisa Lawrence, a public affairs officer at the Pentagon, said the department plans to continue pursuing reforms as spelled out in the fiscal 2017 defense policy bill.

“The Department remains focused on ensuring the Services maintain a medically ready force and a ready medical force, as well as [ensuring] all eligible beneficiaries have continued access to quality health care,” Lawrence said.

A staff member for the National Military Family Association said that it “makes sense” the pandemic would lead to a reevaluation of the military health system reforms, adding that the organization hopes the DoD, DHA and military services will continue focusing on accountability, transparency and standardization across the system.

“Whatever the outcome, our priority is that service members and families have access to high-quality health care, wherever they happen to be stationed,” said Eileen Huck, deputy director for health care at NMFA.

— Patricia Kime can be reached at Patricia.Kime@Monster.com. Follow her on Twitter @patriciakime.

Article link: https://www.military.com/daily-news/2020/08/10/service-chiefs-secdef-stop-handover-of-military-hospitals-defense-health-agency.html

by Freek Vermeulen
November 08, 2017

Many strategy execution processes fail because the firm does not have something worth executing.

Executive Summary

Many strategy execution processes fail because “new strategies” are often not strategies at all. A real strategy involves a clear set of choices that define what the firm is going to do and what it’s not going to do. Many strategies fail to get implemented because they do not represent such a set of clear choices. And many so-called strategies are in fact goals. “We want to be the number one or number two in all the markets in which we operate” is one of those. It does not tell you what you are going to do; all it does is tell you what you hope the outcome will be. But you’ll still need a strategy to achieve it. Another reason why many implementation efforts fail is that executives see it as a pure top-down, two-step process: “The strategy is made; now we implement it.” That’s unlikely to work. A successful strategy execution process is seldom a one-way trickle-down cascade of decisions.

America’s health-care system, the most expensive in the world, is under renewed scrutiny because of the coronavirus pandemic. U.S. employers and households spend almost $4 trillion annually on medical care, yet America regularly lags its peers in key health metrics, and it registered the greatest number of confirmed coronavirus cases and deaths anywhere in the first six months of the global crisis. In advance of a presidential election in November, the virus’s assault shined a spotlight on the gaps and inequities in the market-driven approach of the only rich country not to have universal health care.

1. How is U.S. health care different?

Government involvement in health care goes against the libertarian streak that distinguishes the U.S. from, say, the U.K. and Canada, whose state-funded health systems guaranteeing care for all are derided by some Americans as “socialized medicine.” Only about 36% of Americans, mainly the elderly and poor, receive health-care coverage through the government, via the Medicare and Medicaid programs. More than half of Americans have health insurance as a benefit through work (and can lose coverage if laid off). The 2010 Affordable Care Act, more commonly called Obamacare, has helped about 20 million Americans get health coverage by expanding access to Medicaid and subsidizing purchases of individual plans. Still, as of 2018, about 9% of the population, or 28.3 million people, had no health insurance.

2. What do the uninsured do?

About 1-in-4 put off seeking care in 2018 because of the expense, according to the Centers for Disease Control and Prevention. When emergencies arise, those lacking insurance often seek treatment at hospitals, which by law can’t turn them away. Even among those with insurance, about 29% were “underinsured” in 2018, meaning they faced high out-of-pocket costs when seeking care, according to the Commonwealth Fund, a private foundation. Those costs can total about $650 a year on average for the non-elderly and can reach into many thousands of dollars in the event of a “surprise billing,” when a patient receives care, often in an emergency, from a provider that’s not covered by the patient’s insurer. Medical expenses or health-related income loss resulted in an average of 530,000 bankruptcies each year in the U.S. from 2013 to 2016, according to the American Public Health Association.

3. What happened when the virus hit?

America’s patchwork system created confusion, muddling the response. Since prices are unregulated, concerns about out-of-pocket costs for coronavirus tests lingered even after federal officials assured Americans they’d pay nothing. The federal government also promised to pay the hospital bills of uninsured Covid-19 patients, and major insurers eventually pledged to waive out-of-pocket hospital expenses for their customers, but concerns about unexpected bills remained. The virus took a particularly harsh toll on Black Americans, who, as a result of income inequality and disparities in access to health care, are more likely to have underlying conditions such as diabetes, hypertension and lung disease. Some state governors bemoaned the lack of a coordinated federal response to challenges such as ensuring sufficient supplies of tests, ventilators and personal protective equipment for health-care workers, tasks that were left to underfunded statelevel public-health departments. The U.S. spent 17% of gross domestic product on health care in 2019, double the average of the well-to-do members of the Organization for Economic Cooperation and Development. But it allots only 3 cents out of every dollar to public health, the field devoted to protecting entire populations by, among other things, responding to infectious disease.

4. Where does U.S. health spending go?

The system encourages more expensive, specialized treatment over primary care. This does make the U.S. a leader in many aspects of medicine. It’s long been at the forefront of research and has lower death rates for breast cancer, heart attacks and strokes. Patients face shorter wait times to see specialists and can have access to state-of-the-art procedures. U.S. doctors earn roughly twice as much as those in other wealthy countries. At the same time, the U.S. has some of the worst health outcomes, including the lowest life expectancy among its peers and the highest rate of “avoidable deaths” — those that could have been prevented with effective care.

5. How is health care an election issue?

President Donald Trump and the Republican Party generally regard Obamacare as government overreach and support a challenge before the Supreme Court that could gut it. Some Democrats want to abolish the existing system of private insurance in favor of a government program along the lines of the U.K.’s. Trump’s presumptive Democratic challenger, former Vice President Joe Biden, doesn’t go that far but does want to lower the age for Medicare eligibility to 60 from 65 and create a government-provided health plan — a “public option” — that Americans could buy into.

The Reference Shelf

• QuickTake explainers on surprise billing, what’s left of Obamacare and what’s meant by “Medicare for all.”
• These charts show the disproportionate effect of Covid-19 in majority-Black counties.
• How U.S. health care puts $4 trillion in all the wrong places.

— With assistance by John Tozzi

Article link: https://www.bloomberg.com/news/articles/2020-07-29/what-made-u-s-health-care-so-vulnerable-to-covid-19-quicktake?