Kaiser sees net income top $8B in 2021, operating income fall sharply

Kaiser sees net income top $8B in 2021, operating income fall sharply -  NewsBreak

Driven by strong investment gains, Oakland, Calif.-based Kaiser Permanente recorded a net income of $8.1 billion in 2021, an increase of $1.7 billion from 2020, according to its financial results released Feb. 11. However, its operating income fell sharply.

For the 12 months ended Dec. 31, the integrated healthcare provider with 39 hospitals recorded an operating revenue of $93.1 billion, up from $88.7 billion recorded last year. Additionally, Kaiser saw its expenses rise 6.9 percent to $92.5 billion in 2021. 

In 2021, Kaiser saw its operating income fall to $611 million, an operating margin of 0.7 percent. This compares to a $2.2 billion operating income in 2020 and an operating margin of 2.5 percent. 

Kaiser attributed the sharp decrease in operating income to an increase in care delivery expenses due to COVID-19 surges.

Total other income and expenses, which includes investment income, reached $7.5 billion in 2021. In 2020, Kaiser saw a gain of $4.1 billion.

Our financial performance underscores the strength of our integrated model, which allows us to weather unexpected challenges such as the COVID-19 pandemic while continuing to serve our members,” said Kathy Lancaster, Kaiser Permanente executive vice president and CFO.

In 2021, Kaiser also said its health plan membership grew by 185,000 members. It now has more than 12.5 million members.

Read more here.

Cartoon – Knowing What the Numbers Mean

Means Cartoons and Comics - funny pictures from CartoonStock

How to gauge your hospital’s financial health

https://www.beckershospitalreview.com/how-to-gauge-your-hospital-s-financial-health.html

How to gauge your hospital's financial health

Some rural hospitals that were already struggling are now in serious financial trouble due to the coronavirus.

The suspension of elective surgery and non-urgent care in most states led to an abrupt drop in patient volumes and hospital revenue. That loss, combined with the cost of preparing for COVID-19 protections for patients and employees, has forced rural hospitals into deeper distress. It’s especially important in these challenging circumstances to keep a close eye on key metrics that gauge a hospital’s financial health. By monitoring indicators, creating transparency and responding swiftly to warning signals of financial distress, hospitals can stave off bankruptcy or closure and establish a new path toward long-term sustainability. 

A Shared Responsibility

Signs that a hospital is headed for, or already in, financial distress include obvious indicators such as declining revenues or a dip in patient volume. Although some distress signals seem loud and clear, problems persist at many hospitals due to lack of communication and financial assessment across the enterprise. Too often, it’s left to the CFO to monitor overall financial health by measuring against budgets and recent trends. However, a regular review of key metrics should be a shared responsibility for the entire healthcare leadership team.

Five Data Points to Review

Hospitals may need to adjust key targets to bring them in line with what’s realistically achievable while the pandemic persists, particularly when it comes to productivity, PPE costs and net revenue metrics. Think wisely and as a team about how to reassess targets. The following data points should be monitored regularly. 

  1. Aggregate volume and provider utilization trends. This data can offer a big-picture perspective to leaders and managers across departments.
  2. Operating ratios, including expenses as a percentage of net operating revenue. Make sure costs such as labor, supplies and purchased services remain in check. 
  3. Labor costs relative to patient volume. Measure productivity in each department against department specific staffing targets as well as the overall FTE per adjusted occupied bed target for the hospital as a whole.
  4. Patient revenue indicators. These include bad debt percentage and net to gross percentage by payer class. Are there shifts in payer mix that need to be addressed?
  5. Liquidity ratios. These include net days in patient accounts receivable and cash collections as a percentage of net revenue. What steps can be taken to improve cash flow?

Information Gathering

Hospital leadership should conduct a monthly review of the key measures listed above. In addition, procedures should be put in place by the hospital’s finance department, with input from department managers, to produce accurate monthly stats and financial performance metrics to facilitate these periodic reviews. Annually, take a closer look at these financial indicators, as these will form the basis of strategic planning. 

Federal Funding

The COVID-19 crisis reinforces the need for financial diligence and discipline. Rural hospitals received federal funding to help them during the crisis, and this created another layer of data to monitor. Whether in the form of a CARES Act grant, a PPP loan or some other type of funding, these outlays must be closely controlled, properly managed and restricted in use so the hospital does not run out of cash. In certain cases, the federal government will require hospitals to document the use of funds. For example, for CARES Act stimulus payments, hospitals must provide attestation (quarterly beginning in July) that funds are used for COVID-related costs and COVID-related loss of revenue. In any case, CHC recommends that hospitals set up a tracking system to account for these funds. Download a financial dashboard to help.

Connect the Dots

Regular reviews of financial indicators can identify operational best practices, support strategic planning efforts, create accountability, and, if necessary, redirect financial sustainability efforts. The COVID-19 crisis accelerates the timeline during which financial improvements must be made. 

The most critical element of this entire process is answering, “Why?” This means finding the root causes for financial difficulties. Another critical element is clear communication of expectations and goals across hospital leadership in order to accomplish desired changes. The team, armed with data and clear objectives, can then get to the root of any problems. 

Covid-19 Data in the US Is an ‘Information Catastrophe’

https://www.wired.com/story/covid-19-data-in-the-us-is-an-information-catastrophe/#intcid=recommendations_wired-bottom-recirc-personalized_31e95638-88d6-439c-85a2-db8f6235da26_text2vec1-mab

Covid-19 Data in the US Is an 'Information Catastrophe' | WIRED

The order to reroute CDC hospitalization figures raised accuracy concerns. But that’s just one of the problems with how the country collects health data.

TWO WEEKS AGO, the Department of Health and Human Services stripped the Centers for Disease Control and Prevention of control of national data on Covid-19 infections in hospitalized patients. Instead of sending the data to the CDC’s public National Healthcare Safety Network (NHSN), the department ordered hospitals to send it to a new data system, run for the agency by a little-known firm in Tennessee.

The change took effect immediately. First, the hospitalization data collected up until July 13 vanished from the CDC’s site. One day later, it was republished—but topped by a note that the NHSN Covid-19 dashboard would no longer be updated.

Fury over the move was immediate. All the major organizations that represent US public health professionals objected vociferously. A quickly written protest letter addressed to Vice President Mike Pence, HHS secretary Alex Azar, and Deborah Birx, the coordinator of the White House’s Coronavirus Task Force, garnered signatures from more than 100 health associations and research groups. The reactions made visible the groups’ concerns that data could be lost or duplicated, and underlined their continual worry that the CDC is being undercut and sidelined. But it had no other effect. The new HHS portal, called HHS Protect, is up and running.

Behind the crisis lies a difficult reality: Covid-19 data in the US—in fact, almost all public health data—is chaotic: not one pipe, but a tangle. If the nation had a single, seamless system for collecting, storing, and analyzing health data, HHS and the Coronavirus Task Force would have had a much harder time prying the CDC’s Covid-19 data loose. Not having a comprehensive system made the HHS move possible, and however well or badly the department handles the data it will now receive, the lack of a comprehensive data system is harming the US coronavirus response.

“Every health system, every public health department, every jurisdiction really has their own ways of going about things,” says Caitlin Rivers, a senior scholar at the Johns Hopkins Center for Health Security. “It’s very difficult to get an accurate and timely and geographically resolved picture of what’s happening in the US, because there’s such a jumble of data.”

Data systems are wonky objects, so it may help to step back and explain a little history. First, there’s a reason why hospitalization data is important: Knowing whether the demand for beds is rising or falling can help illuminate how hard-hit any area is, and whether reopening in that region is safe.

Second, what the NHSN does is important too. It’s a 15-year-old database, organized in 2005 out of several streams of information that were already flowing to the CDC, which receives data from hospitals and other health care facilities about anything that affects the occurrence of infections once someone is admitted. That includes rates of pneumonia from use of ventilators, infections after surgery, and urinary tract infections from catheters, for instance—but also statistics about usage of antibiotics, adherence to hand hygiene, complications from dialysis, occurrence of the ravaging intestinal infection C. difficile, and rates of health care workers getting flu shots. Broadly, it assembles a portrait of the safety of hospitals, nursing homes, and chronic care institutions in the US, and it shares that data with researchers and with other statistical dashboards published by other HHS agencies such as the Center for Medicare and Medicaid Services.

Because NHSN only collects institutional data, and Covid-19 infections occur both inside institutions such as nursing homes and hospitals, and in the outside world, HHS officials claimed the database was a bad fit for the coronavirus pandemic. But people who have worked with it argue that since the network had already devised channels for receiving all that data from health care systems, it ought to continue to do so—especially since that data isn’t easy to abstract.

“If you are lucky enough to work in a large health care system that has a sophisticated electronic medical record, then possibly you can push one button and have all the data flow up to NHSN,” says Angela Vassallo, an epidemiologist who formerly worked at HHS and is now chief clinical adviser to the infection-prevention firm Covid Smart. “But that’s a rare experience. Most hospitals have an infection preventionist, usually an entire team, responsible for transferring that data by hand.”

There lies the core problem. Despite big efforts back during the Obama administration to funnel all US health care data into one large-bore pipeline, what exists now resembles what you’d find behind the walls of an old house: pipes going everywhere, patched at improbable angles, some of them leaky, and some of them dead ends. To take some examples from the coronavirus response: Covid-19 hospital admissions were measured by the NHSN (before HHS intervened), but cases coming to emergency departments were reported in a different database, and test results were reported first to local or state health departments, and then sent up to the CDC.

Covid-19 data in particular has been so messy that volunteer efforts have sprung up to fix it. These include the COVID Tracking Project—compiled from multiple sources and currently the most comprehensive set of statistics, used by media organizations and apparently by the White House—and Covid Exit Strategy, which uses data from the COVID Tracking Project and the CDC.

Last week, the American Public Health Association, the Johns Hopkins Center, and Resolve to Save Lives, a nonprofit led by former CDC director Tom Frieden, released a comprehensive report on Covid-19 data collection. Pulling no punches, they called the current situation an “information catastrophe.”

The US, they found, does not have national-, state-, county-, or city-level standards for Covid-19 data. Every state maintains some form of coronavirus dashboard (and some have several), but every dashboard is different; no two states present the same data categories, nor visualize them the same way. The data presented by states is “inconsistent, incomplete, and inaccessible,” the group found: Out of 15 key pieces of data that each state should be presenting—things such as new confirmed and probable cases, new tests performed, and percentage of tests that are positive—only 38 percent of the indicators are reported in some way, with limitations, and 60 percent are not reported at all.

“This is not the fault of the states—there was no federal leadership,” Frieden emphasized in an interview with WIRED. “And this is legitimately difficult. But it’s not impossible. It just requires commitment.”

But the problem of incomplete, messy data is older and deeper than this pandemic. Four scholars from the health-policy think tank the Commonwealth Fund called out the broader problem just last week in an essay in The New England Journal of Medicine, naming health data as one of four interlocking health care crises exposed by Covid-19. (The others were reliance on employer-provided health care, financial losses in rural and primary-care practices, and the effect of the pandemic on racial and ethinic minorities.)

“There is no national public health information system—electronic or otherwise—that enables authorities to identify regional variation in the demand for, and supply of, resources critical to managing Covid-19,” they wrote. The fix they recommended: a national public health information system that would record diagnoses in real time, monitor the materials hospitals need, and link hospitals and outpatient care, state and local health departments, and laboratories and manufacturers to maintain real-time reporting on disease occurrence, preventive measures, and equipment production.

They are not the first to say this is needed. In February, 2019, the Council of State and Territorial Epidemiologists launched a campaign to get Congress to appropriate $1 billion in new federal funding over 10 years specifically to improve data flows. “The nation’s public health data systems are antiquated, rely on obsolete surveillance methods, and are in dire need of security upgrades,” the group wrote in its launch statement. “Sluggish, manual processes—paper records, spreadsheets, faxes, and phone calls—still in widespread use, have consequences, most notably delayed detection and response to public health threats.”

Defenders of the HHS decision to switch data away from the CDC say that improving problems like that is what the department was aiming for. (“The CDC’s old hospital data-gathering operation once worked well monitoring hospital information across the country, but it’s an inadequate system today,” HHS assistant secretary for public affairs Michael Caputo told CNN.) If that’s an accurate claim, during a global pandemic is a challenging time to do it.

“We were opposed to this, because trying to do this in the middle of a disaster is not the time,” says Georges Benjamin, a physician and executive director of the American Public Health Association, which was a signatory to the letter protesting moving data from the NHSN. “It was just clearly done without a lot of foresight. I don’t think they understand the way data moves into and through the system.”

The past week has shown how correct that concern was. Immediately after the switch, according to CNBC, states were blacked out from receiving data on their own hospitals, because the hospitals were not able to manage the changeover from the CDC to the HHS system. On Tuesday, Ryan Panchadsaram, cofounder of Covid Exit Strategy and former deputy chief technology officer for the US, highlighted on Twitter that data on the HHS dashboard, advertised as updating daily, was five days old. And Tuesday night, the COVID Tracking Project staff warned in a long analysis: “Hospitalization data from states that was highly stable a few weeks ago is currently fragmented, and appears to be a significant undercount.”

When the Covid-19 crisis is over, as everyone hopes it will be someday, the US will still have to wrestle with the questions it raised. One of those will be how the richest country on the planet, with some of the best clinical care in the world, was content with a health information system that left it so uninformed about a disease affecting so many of its citizens. The answer could involve tearing the public-health data system down and building it again from scratch.

“This is a deeply entrenched problem, where there is no single person who has not done their job,” Rivers says. “Our systems are old. They were not updated. We haven’t invested in them. If you’re trying to imagine a system where everyone reports the same information in the same way and we can push a button and have all the information we might want, that will take a complete overhaul of what we have.”

 

 

 

 

10 best, worst states for healthcare in 2020

https://www.beckershospitalreview.com/rankings-and-ratings/10-best-worst-states-for-healthcare-in-2020-080320.html

2020's Best & Worst States for Health Care

Americans in Massachusetts receive the best healthcare in the country, while those in Georgia receive the worst, according to an analysis by WalletHub, a personal finance website. 

To identify the best and worst states for healthcare, analysts compared the 50 states and the District of Columbia across 44 different measures of healthcare cost, access and outcomes. The metrics ranged from average hospital expenses per inpatient day to share of patients readmitted to hospitals. Read more about the methodology here.

Here are the 10 states with the highest overall rank across cost, access and outcomes, according to the analysis: 

1. Massachusetts

2. Minnesota

3. Rhode Island

4. District of Columbia

5. North Dakota

6. Vermont

7. Colorado

8. Iowa

9. Hawaii

10. South Dakota

Here are the bottom 10 states on healthcare cost, access and outcomes combined:

1. Georgia

2. Louisiana

3. Alabama

4. North Carolina

5. Mississippi

6. Arkansas

7. Tennessee

8. South Carolina

9. Texas

10. Alaska

Access the full list here

 

 

 

 

Navigating a Post-Covid Path to the New Normal with Gist Healthcare CEO, Chas Roades

https://www.lrvhealth.com/podcast/?single_podcast=2203

Covid-19, Regulatory Changes and Election Implications: An Inside ...Chas Roades (@ChasRoades) | Twitter

Healthcare is Hard: A Podcast for Insiders; June 11, 2020

Over the course of nearly 20 years as Chief Research Officer at The Advisory Board Company, Chas Roades became a trusted advisor for CEOs, leadership teams and boards of directors at health systems across the country. When The Advisory Board was acquired by Optum in 2017, Chas left the company with Chief Medical Officer, Lisa Bielamowicz. Together they founded Gist Healthcare, where they play a similar role, but take an even deeper and more focused look at the issues health systems are facing.

As Chas explains, Gist Healthcare has members from Allentown, Pennsylvania to Beverly Hills, California and everywhere in between. Most of the organizations Gist works with are regional health systems in the $2 to $5 billion range, where Chas and his colleagues become adjunct members of the executive team and board. In this role, Chas is typically hopscotching the country for in-person meetings and strategy sessions, but Covid-19 has brought many changes.

“Almost overnight, Chas went from in-depth sessions about long-term five-year strategy, to discussions about how health systems will make it through the next six weeks and after that, adapt to the new normal. He spoke to Keith Figlioli about many of the issues impacting these discussions including:

  • Corporate Governance. The decisions health systems will be forced to make over the next two to five years are staggeringly big, according to Chas. As a result, Gist is spending a lot of time thinking about governance right now and how to help health systems supercharge governance processes to lay a foundation for the making these difficult choices.
  • Health Systems Acting Like Systems. As health systems struggle to maintain revenue and margins, they’ll be forced to streamline operations in a way that finally takes advantage of system value. As providers consolidated in recent years, they successfully met the goal of gaining size and negotiating leverage, but paid much less attention to the harder part – controlling cost and creating value. That’s about to change. It will be a lasting impact of Covid-19, and an opportunity for innovators.
  • The Telehealth Land Grab. Providers have quickly ramped-up telehealth services as a necessity to survive during lockdowns. But as telehealth plays a larger role in the new standard of care, payers will not sit idly by and are preparing to double-down on their own virtual care capabilities. They’re looking to take over the virtual space and own the digital front door in an effort to gain coveted customer loyalty. Chas talks about how it would be foolish for providers to expect that payers will continue reimburse at high rates or at parity for physical visits.
  • The Battleground Over Physicians. This is the other area to watch as payers and providers clash over the hearts and minds of consumers. The years-long trend of physician practices being acquired and rolled-up into larger organizations will significantly accelerate due to Covid-19. The financial pain the pandemic has caused will force some practices out of business and many others looking for an exit. And as health systems deal with their own financial hardships, payers with deep pockets are the more likely suitor.”

 

 

 

 

Healthcare mergers and acquisitions require sensible data sharing strategies, and a solid analytics framework

https://www.healthcarefinancenews.com/news/healthcare-mergers-and-acquisitions-require-sensible-data-sharing-strategies-and-solid?mkt_tok=eyJpIjoiTmpJME5qVTNOVEU1TXpRdyIsInQiOiJDdUIxQ1NKdng1b0FkQ1wvQlwvNFBTc1JIbmVwYUZOeUhCZ3VlNlZzdmhNbkhBQlhnXC9JeTI4c2NDeE80REk0YWJ1Nk1jSzl4QjFDbjFMTkxKdmVCblY1RUlSYTIwUmlhSEJ6VXpkOUZZdytUWDhaV1poaEljcVh5ZFdEOUdVZlQzZyJ9

While it’s important for disparate EHRs to communicate with one another, organizations need a better handle on analytics and dashboards.

Mergers and acquisitions in healthcare have been going along at a pretty good clip for a number of years now. The volume of deals remains high, and with larger entities primed to scoop up some of their smaller, struggling peers, the trend seems poised to continue.

There’s an issue that consolidating organizations consistently run into, however: data sharing.

Specifically, many organizations that have initiated merger activity fail to consider that not only will the consolidation necessitate integrating multiple electronic health records, but other ancillary systems as well.

These organizations need to produce the analytics that are required to manage what’s essentially a new business, and that starts with the development of some sort of analytics blueprint early on in the merger activity.

As two or more forces join into one, it’s important to have the analytics blueprint in place so leaderships knows which dashboards are going to be needed for success.

“There used to be a trend where everyone was converted onto the same EHR platform,” said John Walton, solutions architect for IT consulting company CTG. “I guess the thought is that if everyone is converted, the problem will go away. Now … they end up in a situation where they can’t produce the kind of dashboards that are needed.”

THE FRAMEWORK

A key component of an effective analytics blueprint is a conceptual data model — basically a visual representation of what domains are needed for the dashboards.

“It sounds difficult to produce, but if it takes more than four to six weeks to produce something like that, you’re overthinking it,” said Walton. “But that’s then starting point. The key component is that analytics framework.”

Failure to have a framework in place can result in the newly merged entity losing out in terms of revenue and productivity. And once the problem becomes manifest, there’s often a lot of manual effort that goes into serving, for example, the financial dashboards that are so needed by CFOs. A lot of the manual effort goes into putting the data into Excel spreadsheets, which only puts a Band-Aid on the problem.

“The framework essentially provides pre-built routines to extract data from multiple data sources, as well as from financial systems,” said Walton. “It also provides, for lack of a better term, the data plumbing to enterprise standards, and most importantly there’s an analytic layer. The endgame is that the dashboards need to sit on top of an analytics layer that is easy to do analytics on. What it contains is pre-computed performance indicators based on approved business rules with multiple levels of aggregation.”

An effective framework, as with so many other things, begins with C-suite leadership. Having executive sponsorship, or at least an understanding of the issue at an executive level, can translate into a vision for how to integrate the data and provide the analytics that are needed to successfully manage the business.

PLANNING PROACTIVELY

Walton once observed a national organization that acquired another entity, and after two years the CEO still didn’t have any executive dashboards — which means a lack of visibility into the performance metrics. The CEO hen issued what was effectively a mandate to the acquiring organization: Get this done within three months, or else.

Thus began a flurry of activity to et the dashboard situation straightened out, which is not where an organization wants to be. Proactive planning is essential, yet Walton doesn’t see a lot of that in healthcare.

“I’ve never seen an organization proactively plan for this,” he said. “That doesn’t mean it’s not happening, but in my experience I haven’t seen it.”

In the meantime, mergers and acquisitions keep happening. Even if merging organizations become aware of the problem and factor that into their decision-making there’s another issue to consider.

“Another extremely significant problem is data quality and information consistency,” said Walton. “That problem really is not universally dealt with, in healthcare or for that matter other industries. It’s almost like they’ve learned to live with it. It’s almost like we need a call to arms or something. You’re almost certainly going to have the need for an analytics framework that will apply the data to your standards.”

The data in question could encompass missing or clinically inappropriate data. Quality, in this case, has to do with the cleanliness of the data. In terms of consistency, a good example would be something like average length of stay. There’s an opportunity to ensure that the right data ownership and stewardship is in place.

Importantly, it’s primarily a business solution. It’s possible that one of the merging entities has a data governance strategy, but all too often that strategy was launched by the IT department — which is not where an organization wants to be, said Walton, because it’s primarily a business problem rather than one that’s purely technical.

Data governance is a very well-known concept, but people struggle with its actual implementation for a number of reasons,” he said. “One, there’s a technical aspect of it, which centers around how we identify data quality issues. What kinds of tools are they going to use to address data quality issues?

“Then there’s establishing ownership of the data, and who are the subject matter experts. And there’s a workflow aspect that most organizations fail to deal with.”

It all starts with the framework. Only then can merging organizations get an appropriate handle on its data and analytics landscape.