Hospitals are increasingly offering 'patient-financing' strategies, cooperating with financial institutions to offer on-the-spot loans to make sure patients pay their bills.

This article first appeared February 21, 2018 on Kaiser Health News.

By Shefali Luthra

Laura Cameron, then three months pregnant, tripped and fell in a parking lot and landed in the emergency room last May — her blood pressure was low and she was scared and in pain. She was flat on her back and plugged into a saline drip when a hospital employee approached her gurney to discuss how she would pay her hospital bill.

Though both Cameron, 28, and her husband, Keith, have insurance, the bill would likely come to about $830, the representative said. If that sounded unmanageable, she offered, they could take out a loan through a bank that had a partnership with the hospital.

The hospital employee was “fairly forceful,” said Cameron, who lives in Fayetteville, Ark. “She certainly made it clear she preferred we pay then, or we take this deal with the bank.”

Hospitals are increasingly offering “patient-financing” strategies, cooperating with financial institutions to offer on-the-spot loans to make sure patients pay their bills.

Private doctors’ offices and surgery centers have long offered such no- or low-interest financing for procedures not covered by insurance, like plastic surgery, or to patients paying themselves for an expensive test or procedure with a fixed price.

But promoting bank loans at hospitals and, particularly, emergency rooms raises concerns, experts say. For one thing, the cost estimates provided — likely based on a hospital’s list price — may be far higher than the negotiated rate ultimately paid by most insurers. Sick patients, like Cameron, may feel they have no choice but to sign up for a loan since they need treatment. And the quick loan process, usually with no credit check, means they may well be signing on for expenses they can ill afford to pay.

The offers may sound like a tempting solution for scared, vulnerable patients, but they may not be such a great bargain, suggests Mark Rukavina, an expert in medical debt and billing at Community Catalyst, a Boston-based advocacy group.

His point: “If you pay zero percent interest on a seriously inflated charge, it’s not a good deal.”

How The Loans Work

Between higher deductibles and narrower networks, patients are paying larger portions of their medical bills. The federal government estimates consumers spent $352.5 billion out-of-pocket on health care in 2016.

But many patients have trouble coming up with cash to pay bills of hundreds or even thousands of dollars, meaning hospitals are having a harder time collecting what they believe they are owed.

To solve their problem, about 15 to 20 percent of hospitals are teaming up with lenders to offer loans, said Bruce Haupt, CEO of ClearBalance, a loan servicing company. He, along with many other analysts, expects that percentage to grow.

The process begins with a hospital estimate of a patient’s bill, which takes insurance coverage into account. A billing representative then lays out payment plans for the patient, often while he or she is still being treated.

A patient can then sign up for a loan, often without a credit check. Patients write smaller monthly checks to the lender, who has paid the hospital, while keeping a designated percentage of the bill as a fee.

Proponents view financing as a useful alternative to medical credit cards, which can surprise users with high interest rates. The partnerships are tempting for hospitals since they offload the need to administer monthly payment plans and collection efforts.

Federal law requires lenders be transparent about the loan terms, a protection that extends to consumers entering these health care arrangements. That means disclosure of interest rates, other fees and the payment schedule.

Even so, said Gerard Anderson, a Johns Hopkins health policy professor and an expert on health care pricing, “it’s an often gentler version of asking you to pay up.”

But an on-the-stretcher sell leaves patients little opportunity for due diligence.

“What’s the charge they’re using to determine what’s a reasonable amount to pay?” Anderson added.

Cameron was suspicious of the $830 estimate of her bill, since she had good coverage from her job at the University of Arkansas. She and her husband had extensive experience with the health care system and its costs. No one had ever asked her to pay upfront, even when her husband owed tens of thousands for cancer treatment.

“It just felt so uncomfortable to us that they would try to push us through a bank, which is designed to make a profit,” Cameron said.

A Growth Business With Risk Of Default

At Florida-based Orlando Health, which works with ClearBalance, loans typically range from $3,000 to $7,000, said Michele Napier, the health system’s chief revenue officer. The highest debt a patient has taken on — about $13,000 — was because of a high-deductible plan, she said.

“All of a sudden a catastrophic event occurs, and to have $13,000 in the bank account is a lot to ask,” she said. “They’re able to spread those payments.”

Low-income patients without insurance likely will not need loans to finance large bills,because they should quality for aid from the hospital, or be treated as charity care, Napier said.

It’s a conversation that starts at registration, she added. “If a patient shares with us that they have no resources or limited resources to pay, we will provide information on our financial assistance and other programs including screening them for Medicaid.”

The idea is to foster open conversations about cost and help patients and doctors weigh their options, both financial and medical, said Rick Gundling, a senior vice president at the Healthcare Financial Management Association, a trade group.

“The patient may say, ‘Hey, do I need to do this knee surgery now? Can we wait until I save up, or do I have other options, like physical therapy?’” he said. “The doctor may say … let’s look at other options.”

But the loans can be a band-aid solution, leading vulnerable patients to sign up to pay far more than they should, said Kathleen Engel, a research professor of law at Boston-based Suffolk University and an expert in consumer credit and mortgage finance.

“The hospital potentially is charging the patient the full, what I would call ‘whack rate’ for their care,” she said. “They try to collect the debt.”

Since many of these loans come without credit checks or affordability tests, the odds are higher that a loan could be financially unwise, experts warn.

At ClearBalance, loans average about $1,700, Haupt said. In practice, that means some patients are financing $150 bills, while others have them for as large $50,000.

Default rates vary across the country, with the highest default rates — up to 1 in 5 patients — in places such as Texas and Louisiana. In other areas, closer to 6 or 7 percent of patients ultimately cannot pay off their loans.

“Some of these people are destined to default,” Engel said. “If you have to get a loan for $500 for medical care, that means you are really living at the margins.”

Cameron declined the loan — and chose not to hand over any other form of payment. She wanted to wait until she received her insurance statement.

In the end, the couple owed only $150, the copayment for an ER visit. “It felt to us like it could screw someone over who wasn’t aware about how to work that system,” she said, though she admitted to feeling intimidated as she lay on the stretcher.

She added: “It can be scary feeling like you owe someone money.”

For years, American consumers have tried to buy cheaper drugs from their northern neighbor. But the practice is illegal.

This article first appeared February 14, 2018 on Kaiser Health News.

By Shefali Luthra

Norm Thurston is a “free-market guy” — a conservative health economist in Republican-run Utah who rarely sees the government’s involvement in anything as beneficial.

But in a twist, the state lawmaker is now pushing for Utah to flex its muscle to spur federal action on ever-climbing prescription drug prices.

“This is something that a red state like Utah could do. I don’t think this is a partisan issue,” Thurston said. “Those outrageous cost increases are not the result of the free market.”

The approach: Let the state contract with wholesalers in Canada, importing cheaper prescriptions from up north and distributing them to the state’s health care system.

Other states — Vermont, West Virginia and Oklahoma, among them — are following similar paths, pushing legislation that would seek permission from the Trump administration to launch their own plans to import drugs from Canada.

For years, American consumers have tried to buy cheaper drugs from their northern neighbor, sometimes packing into buses for day trips to Canadian pharmacies, or patronizing American stores that help them order drugs from abroad. But the practice is illegal.

The states want to change that, and set up a formal process that nets broader savings. The idea is for the state health department to set up a wholesale program that buys drugs from Canada and resells them to local pharmacies and hospitals. Individual states would be responsible for ensuring that the medications are safe and that importing them does save money.

“This statute is putting pressure on the federal government to take a harder look at these questions,” said Rachel Sachs, an associate law professor at Washington University of St. Louis, who researches drug price regulations. “The state legislatures can say, ‘Look, we’re doing everything we can, but we do need the federal government to help us out on this.’”

The federal government has been slow to act on this issue, and skeptics say a 30-page Trump administration memo on drug pricing released late last week would likely have only limited impact.

But states, whose budgets for Medicaid and state employee health programs are squeezed by these costs, are moving forward.

In Vermont alone, drug spending has gone up by 35 percent from 2010 to 2015, the most recent year for which data are available.

Backers of the state plans say the strategy is a no-brainer that could save hundreds of millions of dollars. They discount concerns about drug safety, arguing that drugs from Canada are made by reputable companies, often in the same facilities and by the same firms that sell them in the U.S. — but at much higher prices.

“We would be bringing in drugs intended for the Canadian market, and therefore at Canadian pricing,” Thurston said. “One would assume if we could come up with a program that meets the recommendations of federal law, what justification would the [Health and Human Services] secretary have for saying no?”

The state measures follow model legislation developed by the National Academy for State Health Policy that uses a framework put in place by the 2003 federal law that created the Medicare Part D program. That law says the U.S. Department of Health and Human Services can approve drug importation plans if it is convinced the plans will save money and will not create any public health concerns.

Once passed, these laws task state health departments with overseeing the development of these programs. After the health department settles on the specifics, state officials must negotiate implementation with HHS. That could take years.

It is also likely to be an uphill battle.

In 15 years, HHS has never acted upon the 2003 law by approving any drug importation program.

Last spring, when members of Congress pushed a national bill, a bipartisan group of former Food and Drug Administration commissioners came out in opposition, arguing it would be impossible to verify drug safety absolutely. That bill ultimately failed to garner a majority vote.

It’s unclear where the current administration stands on this issue.

Alex Azar, the newly confirmed HHS secretary, has been coy on the subject — though in a confirmation hearing last fall, he said importing drugs from Canada could create safety concerns. Despite multiple requests, HHS did not provide comment for this story by the publication deadline.

The pharmaceutical industry echoed the cautions about safety.

“The proposals we are seeing in states across the country threaten the safety of patients and families and will not deliver the savings they promise,” said Priscilla VanderVeer, a spokeswoman for the trade group Pharmaceutical Research and Manufacturers of America (PhRMA).

In the states, though, backers say their bills address that concern.

And other analysts argued that, regardless, safety of Canadian drugs isn’t a real issue.

“A lot of the drugs used in the United States and in Canada are made in the same plants, in countries like India or Europe,” said Michael Law, a pharmaceutical policy expert and associate professor at the University of British Columbia’s Center for Health Services and Policy Research. “The U.S. FDA and other regulatory agencies rely on other agencies’ inspections — the idea that Canadian drugs are these dangerous drugs is a red herring.”

A bigger question, he said, is the amount of savings these bills would generate.

Thurston pointed to Utah state analyses that suggest the state could save $70 million in the private sector, and another $20 million to $30 million in state-funded insurance programs. If approved, he said, the state would target 15 to 20 drugs to import — insulin, for instance, because it is bought in large quantities, or expensive drugs that treat hepatitis C or HIV.

Others expressed skepticism.

For one thing, the true price of prescription drugs isn’t always clear. There’s the list price — and generally, those are much higher in the United States. But insurance plans often negotiate rebates, or discounts, from the drug company — meaning they can end up paying far less than what’s advertised. Those discounts aren’t public, making it much harder to compare prices between the two countries.

The drug industry would also likely employ strategies to counter importation.

Pharmaceutical companies, Law noted, stand to lose if American states are importing cheaper drugs. That could motivate them to tamp down how many prescriptions they sell in Canada, or find other ways to discourage Canadian wholesalers from participating.

“My guess is any Canadian distributor to engage in that would find their [medication] supply dwindle quickly, because the drug companies would stop supplying,” he said. “The supplier systems in the United States would probably find it hard to get a [Canadian drug] supply in the long term.”

That’s certainly a real concern, said Claire Ayer, a Vermont state senator and Democrat who chairs her legislature’s Health and Welfare Committee.

“We can’t tell drug companies or wholesalers what to do in Canada,” she added.

VanderVeer said PhRMA could not speculate on how individual drug companies may react to importation.

Still, these state efforts could spur the federal government to take action, Sachs suggested — even if it’s unclear how large an impact importation would have.

“Importation will not solve all the problems — and I don’t think states see it as such,” she said. “But it could be a useful way to put pressure on a federal government and White House that has thus far largely been inactive on this topic.”

After years of experimentation, machine learning's predictive powers are well-established. Some say it's poised to move from labs to broad real-world applications.

This article first appeared February 14, 2018 on Kaiser Health News.

By John McQuaid

The technology used by Facebook, Google and Amazon to turn spoken language into text, recognize faces and target advertising could help doctors combat one of the deadliest killers in American hospitals.

Clostridium difficile, a deadly bacterium spread by physical contact with objects or infected people, thrives in hospitals, causing 453,000 cases a year and 29,000 deaths in the United States, according to a 2015 study in the New England Journal of Medicine. Traditional methods such as monitoring hygiene and warning signs often fail to stop the disease.

But what if it were possible to systematically target those most vulnerable to C-diff? Erica Shenoy, an infectious-disease specialist at Massachusetts General Hospital, and Jenna Wiens, a computer scientist and assistant professor of engineering at the University of Michigan, did just that when they created an algorithm to predict a patient’s risk of developing a C-diff infection, or CDI. Using patients’ vital signs and other health records, this method — still in an experimental phase — is something both researchers want to see integrated into hospital routines.

The CDI algorithm — based on a form of artificial intelligence called machine learning — is at the leading edge of a technological wave starting to hit the U.S. health care industry. After years of experimentation, machine learning’s predictive powers are well-established, and it is poised to move from labs to broad real-world applications, said Zeeshan Syed, who directs Stanford University’s Clinical Inference and Algorithms Program.

“The implications of machine learning are profound,” Syed said. “Yet it also promises to be an unpredictable, disruptive force — likely to alter the way medical decisions are made and put some people out of work.

Machine learning (ML) relies on artificial neural networks that roughly mimic the way animal brains learn.

As a fox maps new terrain, for instance, responding to smells, sights and noises, it continually adapts and refines its behavior to maximize the odds of finding its next meal. Neural networks map virtual terrains of ones and zeroes. A machine learning algorithm programmed to identify images of coffee cups might compare photos of random objects against a database of coffee cup pictures; by examining more images, it systematically learns the features to make a positive ID more quickly and accurately.

Shenoy and Wiens’ CDI algorithm analyzed a data set from 374,000 inpatient admissions to Massachusetts General Hospital and the University of Michigan Health System, seeking connections between cases of CDI and the circumstances behind them.

The records contained over 4,000 distinct variables. “We have data pertaining to everything from lab results to what bed they are in, to who is in the bed next to them and whether they are infected. We included all medications, labs and diagnoses. And we extracted this on a daily basis,” Wiens said. “You can imagine, as the patient moves around the hospital, risk evolves over time, and we wanted to capture that.”

As it repeatedly analyzes this data, the ML process extracts warning signs of disease that doctors may miss — constellations of symptoms, circumstances and details of medical history most likely to result in infection at any point in the hospital stay.

Such algorithms, now commonplace in internet commerce, finance and self-driving cars, are relatively untested in medicine and health care. In the U.S., the transition from written to electronic health records has been slow, and the format and quality of the data still vary by health system — and sometimes down to the medical practice level — creating obstacles for computer scientists.

But other trends are proving inexorable: Computing power has grown exponentially while getting cheaper. Once, creating a machine learning algorithm required networks of mainframe computers; now it can be done on a laptop.

Radiology and pathology will experience the changes first, experts say. Machine learning programs will most easily handle analyzing images. X-rays and MRI, PET and CT scans are, after all, masses of data. By crunching the data contained in thousands of existing scan images along with the diagnoses doctors have made from them, algorithms can distill the collective knowledge of the medical establishment in days or hours. This enables them to duplicate or surpass the accuracy of any single doctor.

Machine learning algorithms can now reliably diagnose skin cancers (from photographs) and lung cancer, and predict the risk of seizures.

Google research scientist Lily Peng, a physician, led a team that developed a machine learning algorithm to diagnose a patient’s risk of diabetic retinopathy from a retinal scan. DR, a common side effect of diabetes, can lead to blindness if left untreated. The worldwide rise in diabetes rates has turned DR into a global health problem, with the number of cases expected to rise from 126.6 million in 2011 to 191 million by 2030 — an increase of nearly 51 percent. Its presence is indicated by increasingly muddy-looking scan images.

Peng’s team gathered 128,000 retinal scans from hospitals in India and the U.S. and assembled a team of 54 ophthalmologists to grade them on a 5-point scale for signs of the disease. Multiple doctors reviewed each image to average out individual differences of interpretation.

Once “trained” on an initial data set with the diagnoses, the algorithm was tested on another set of data — and there it slightly exceeded the collective performance of the ophthalmologists.

Now Peng is working on applying this tool in India, where a chronic shortage of ophthalmologists means DR often goes undiagnosed and untreated until it’s too late to save a patient’s vision. (This is also a problem in the U.S., where 38 percent of adult diabetes patients do not get the recommended annual eye check for the disease, according to the Centers for Disease Control.)

A group of Indian hospitals is now testing the algorithm. Ordinarily, a scan is done, and a patient may wait days for results after a specialist — if available — reads the image. The algorithm, via software running on hospital computers, makes the results available immediately and a patient can be referred to treatment.

Last year, the Food and Drug Administration approved the first medical machine learning algorithm for commercial use by the San Francisco company Arterys. Its algorithm, “DeepVentricle,” performs in 30 seconds a task doctors typically do by hand — drawing the contours of ventricles from multiple MRI scans of the heart muscle in motion, in order to calculate the volume of blood passing through. That takes an average of 45 minutes. “It’s automating something that is important — and tedious,” said Carla Leibowitz, Arterys’ head of strategy and marketing.

If adopted on a broad scale, such technologies could save lots of time and money. But such change is disruptive.

“The fact that we have identified potential ways to gut out costs is good news. The problem is the people who get gutted are not going to like it — so there will be resistance,” said Eric Topol, director of the Scripps Translational Science Institute. “It undercuts how radiologists do their work. Their primary work is reading scans — what happens when they don’t have to do that?”

The shift may not put a lot of doctors out of work, said Topol, who co-authored a piece in JAMA exploring the issue. Rather, it will likely push them to find new ways to apply their expertise. They may focus on more challenging diagnoses where algorithms continue to fall short, for instance, or interact more with patients.

Beyond this frontier, algorithms can provide a more precise prognosis for the course of a disease — potentially reshaping treatment of progressive ailments or addressing the uncertainties in end-of-life care. They can anticipate fast-moving infections like CDI and chronic ailments such as heart failure.

As the U.S. population ages, heart failure will be a rising burden on the health system and on families.

“It’s the most expensive single disease as a category because of the extreme disability it causes and the high demand for care it imposes, if not managed really tightly,” said Walter “Buzz“ Stewart, vice president and chief research officer at Sutter Health, a health system in Northern California. “If we could predict who was going to get it, perhaps we could begin to intervene much earlier, maybe a year or two years earlier than when it usually happens — when we admit a patient to the hospital after a cardiac event or crash.”

Stewart has collaborated on several studies aiming to address that problem. One, done with Georgia Tech computer scientist Jimeng Sun, predicts whether a patient will develop heart failure within six months, based on 12 to 18 months of outpatient medical records.

These tools, Stewart said, are leading to the “mass customization of health care.” Once algorithms can anticipate incipient stages of conditions like heart failure, doctors will be better able to offer treatments tailored to the patient’s circumstances.

Despite its scientific promise, machine learning in medicine remains terra incognita in many ways. It adds a new voice — the voice of the machine — to key medical decisions, for instance. Doctors and patients may be slow to accept that. Adding to potential doubts, machine learning is often a black box: Data go in, and answers come out, but it’s often unclear why certain patterns in a patient’s data point, say, to an emerging disease. Even the scientists who program neural networks often don’t understand how they reach their conclusions.

“It’s going to make a big difference in how decisions are made — things will become much more data-driven than they used to be,” said John Guttag, a professor of computer science at MIT. Doctors will rely on these increasingly complex tools to make decisions, he said, and “have no idea how they work.” And, in some cases, it will be hard to figure out why bad advice was given.

And while health data are proliferating, the quantity, quality and format vary by institution, and that affects what the algorithms “learn.”

“That is a huge issue with modeling and electronic health records,” Sun said. “Because the data are not curated for research purposes. They are collected as a byproduct of care in day-to-day operations, and utilized mainly for billing and reimbursement purposes. The data is very, very noisy.”

This also means that data may be inconsistent, even in an individual patient’s records. More important, one size does not fit all: An algorithm developed with data from one hospital or health system may not work well for another. “So you need models for different institutions, and the models become quite fragile, you might put it,” Sun said. He is working on a National Institutes of Health grant studying how to develop algorithms that will work across institutions.

And the tide of available medical data continues to rise, tantalizing scientists. “Think about all the data we are collecting right now,” Wiens said. “Electronic health records. Hospitalizations. At outpatient centers. At home. We are starting to collect lots of data on personal monitors. These data are valuable in ways we can’t yet know.”

Suburban areas have historically received a fraction of health funding that cities have, leaving them with inadequate infrastructure and forcing people to scramble for the medical attention they need.

This article first appeared February 09, 2018 on Kaiser Health News.

By Elaine Korry

The promise of cheaper housing brought Shari Castaneda to Palmdale, Calif., in northern Los Angeles County, about nine years ago.

The single mom with five kids had been struggling to pay the bills. “I kept hearing that the rent was a lot cheaper out here, so I moved,” she said.

But when she developed health problems — losing her balance and falling — Castaneda found fewer care options in her new town. Unable to find local specialty care, she traveled nearly 65 miles to a public hospital in Los Angeles, where doctors discovered a tumor on her spine.

Then she had to drive nearly 75 miles to the City of Hope cancer center in Duarte, Calif., for an operation to remove the growth. The procedure left her partially paralyzed. “I walked into the hospital and I never really walked again.”

Castaneda, 58, receives Social Security disability payments and is enrolled in Medi-Cal, the state’s Medicaid program for low-income people. “There are no doctors available here,” said Castaneda. “I called every single one of them in the book, and nobody takes Medi-Cal out here.” Instead, Castaneda now sees doctors nearly 50 miles away in Northridge.

Suburbs in the United States, often perceived as enclaves of the affluent, are home to nearly 17 million Americans who live in poverty — more than in cities or rural areas — and growing demand for care strains the capacity of suburban health services to provide for them, according to a recent study in Health Affairs. Suburban areas have historically received a fraction of health funding that cities have, leaving them with inadequate infrastructure and forcing people like Castaneda to scramble for the medical attention they need.

The Health Affairs study found that about a fifth of the suburban poor are uninsured, and many who do have health insurance — especially people on Medi-Cal — either can’t find providers or must travel far for appointments.

The Affordable Care Act cut California’s uninsured rate from 17 percent in 2013 to about 7 percent last year due largely to the Medicaid expansion, which added more than 3.7 million adults to the state’s Medi-Cal rolls. But that has not ensured access to health care for millions of suburbanites, said Alina Schnake-Mahl, a doctoral candidate at the Harvard T.H. Chan School of Public Health in Boston, who was lead author of the Health Affairs study.

“That really goes against the idea that everyone in the suburbs is insured because everyone has a white-collar job with coverage,” she said.

Coverage doesn’t equate to care even for patients with Medi-Cal, as Castaneda can attest. Before the health law, they had trouble finding doctors who would see them because of Medi-Cal’s low payment rates. That problem intensified as millions more signed up for Medi-Cal, driving many enrollees to seek services at safety-net care facilities.

Health care services in the suburbs “are not robust enough to fill the needs” of a growing low-income population, said Charlie Gillig, supervising attorney at the Health Consumer Center of Neighborhood Legal Services of Los Angeles County, which has advised Castaneda about medical transportation services under Medi-Cal.

One-half of California’s 39 million residents live in suburbs, and rates of poverty among them range from nearly 25 percent around Bakersfield, in the Central Valley, to about 8 percent in the suburbs outside San Francisco, according to an analysis by Elizabeth Kneebone, research director at University of California-Berkeley’s Terner Center for Housing Innovation and a senior fellow at the Brookings Institution. The same analysis showed that 2.7 million suburban Californians lived below the poverty line in 2016, compared with 1.9 million in major cities.

Castaneda, who uses an oversized power wheelchair, says it’s difficult — “often impossible” — to arrange for a ride in a van. Getting to the doctor has become a long, painful ordeal.

And that’s if she can even schedule a visit, said Castaneda, noting that she also faces long wait times for her doctor in Northridge, a suburb that has seen an influx of patients from poorer areas. “You can’t get an appointment when you’re sick … so I’ve just been waiting and waiting,” she said. “They told me, ‘If you get sick enough, just go to the emergency room.’”

Of course, it can also be tough to get a clinic appointment or see a specialist in cities, but in the suburbs, Gillig said, “geography exacerbates an already existing problem.”

In his recent book on the changing geography of poverty, Scott Allard, a professor of public policy and governance at the University of Washington, showed that funding for human services was as much as eight times higher in urban areas than in the suburbs.

California’s metropolitan areas have had many decades to build up massive health care systems to serve the poor, including county hospitals, federally qualified health centers and community clinics. But the current scale of suburban poverty is a recent development.

Policymakers struggle to serve the health needs of cities in eastern Contra Costa County, about 50 miles from San Francisco. In Oakley, for example, business and community leaders lobbied hard for a new health center, which opened in 2011.

“There’s a huge need out here, especially for people who are undocumented or uninsured. They don’t have anywhere else to go,” said Leticia Cazares, regional manager for La Clinica, which operates the new health center. The clinic has two doctors and a nurse practitioner to serve 3,000 patients, most of whom are on Medi-Cal.

Many of the people who visit community clinics like the one in Oakley lack insurance, either because they are undocumented immigrants or because they make too much money to qualify for Medi-Cal — or subsidized coverage under Obamacare — and can’t afford it on their own.

Alex G.’s family fits both scenarios. Her husband, Edward, and 8-year-old son — also named Alex — are U.S. citizens, but she is an undocumented immigrant. The family lives in Brentwood, a town of about 60,000 in eastern Contra Costa County.

A 32-year-old community college student who declined to give her last name for fear of deportation, Alex has applied for permanent residency — a long process with an uncertain outcome.

Her husband has “a good job” as a programmer of industrial machines. He has employer-based insurance, but it covers only him. For Alex and her son to be covered, the family would have to pay $1,200 a month. Given California’s high cost of living, “we just can’t afford to pay that,” Alex said. Her husband’s salary of $70,000 is too high for Medi-Cal or Obamacare subsidies.

Alex recently experienced sharp stomach pains and had to wait several days for a mobile clinic that parks in front of a nearby community center once a week.

Whenever her son has an ear infection or a fever, Alex takes him to the free mobile clinic. “Not having insurance, I worry all the time about him getting sick,” she said.