Category Archives: data saving lives

data saving lives, healthy habits, nutrition, quantified self, rapid learning health systems, research methodology, technology

SMS provides for an effective weight loss intervention

 

Source: http://www.fiercemobilehealthcare.com/story/study-texting-effective-intervention-tool-weight-control/2013-11-21?utm_medium=nl&utm_source=internal

Citation: http://www.jmir.org/2013/11/e244/

Study: Texting effective intervention tool for weight control

November 21, 2013 | By 

Daily text messaging may be a useful self-monitoring tool for weight control, particularly among racial/ethnic minority populations most in need of intervention, according to Duke University study results published in a Journal of Medical Internet Research article.

“Recent studies show that racial/ethnic minorities are more likely than white individuals to own mobile phones,” states the article. “The high familiarity with and penetration of mobile technologies makes text messaging an ideal intervention platform among these populations.”

The purpose of the randomized controlled pilot study was to evaluate the feasibility of a text messaging intervention for weight loss among predominantly black women, who “have alarmingly high rates of obesity as compared with other gender and racial/ethnic groups.” The secondary aim of the study was to evaluate the effects of the intervention on weight change relative to an education control arm.

Fifty obese women aged 25-50 years were randomized to either a six-month intervention using a fully automated system that included daily text messages for self-monitoring tailored behavioral goals (e.g., 10,000 steps per day, no sugary drinks) along with brief feedback and tips (26 women) or to an education control arm (24 women). The article states that weight was objectively measured at baseline and at six months, while adherence was defined as the proportion of text messages received in response to self-monitoring prompts.

At six months, the article reports that the intervention arm lost a mean of 1.27 kg, and the control arm gained a mean of 1.14 kg. The average daily text messaging adherence rate was 49 percent with 85 percent texting self-monitored behavioral goals two or more days per week. Moreover, about 70 percent strongly agreed that daily texting was easy and helpful and 76 percent felt the frequency of texting was appropriate.

“Given that the majority of evidence indicates that greater adherence leads to better outcomes, our study suggests that mHealth-based approaches to self-monitoring may enhance engagement and reduce the burdens commonly associated with other modes,” concluded the article. “Our intervention was relatively low intensity and has greater potential for dissemination compared to higher intensity interventions. As technology penetration increases in the target population, the use of this modality will become increasingly relevant and helpful as an intervention tool for weight control.”

Earlier this year, an article published in the Journal of American Medical Informatics Association revealed that mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in a weight loss program are more effective than traditional methods. The study involved a post hoc analysis of a six-month randomized weight loss trial among 96 overweight men and women, which found that physical activity app users self-monitored exercise more frequently over the six-month study and reported greater intentional physical activity than non-app users.

To learn more:
– read the article in JMIR

data saving lives, healthcare, policy, rapid learning health systems

Building a bridge to the future with population health analytics…

  • Leading US providers are using analytics to bring a more intense focus on gaps in care, to discover cost outliers, and to put a magnifying glass on efficiency
  • “Unlike other industries that may be high users of data and very sophisticated, the healthcare industry is at a different point”
  • “A platform where we mesh both claims data and data out of our electronic health records allows a lot more to be learned. The type of intelligence that we can glean is at a much more informed level than if we’re just dealing with one of those data sets in isolation.”
  • At the heart of population health analytics is the concept of risk stratification: understanding, through various inputs such as claims data, surveys, and EHRs, which members of a given healthcare organization’s customer base represent a level of risk for which intervention offers the greatest possibility of preventing future hospital admissions, reducing readmissions, improving overall health, and lowering costs.
  • Cleveland Clinic’s Explorys pulls data from a variety of sources—multiple electronic health records, billing systems, claims data from CMS and other payers—and assimilates that all together to allow filtering, reporting, identify care gaps and registry functions
  • A variety of tools exist to help stratify risk:

> Some tools place members of a population on a scatter plot to make the identification of outliers easier
> Other tools organize a population into patient registries to track various diseases and treatments
> Still other tools use input gathered from patient surveys.

  • near-real time data is an important addition

 

http://www.healthleadersmedia.com/content/TEC-298525/How-Population-Health-Analytics-Opens-Opportunities-for-Better-Care

How Population Health Analytics Opens Opportunities for Better Care

Scott Mace, for HealthLeaders Media , November 20, 2013

Innovators are blending technology with new care models while targeting high-risk patients in a patient-centered strategy.

This article appears in the November issue of HealthLeaders magazine.

Without robust analytics technology, the goals of accountable care and population health cannot fully be achieved, good intentions notwithstanding. ACOs must correlate clinical data and claims data and use analytics technology to produce the actions needed to manage the health of a population. The data is there, but the healthcare industry does not have an evenly distributed knowledge of how to use it effectively.

With potential savings of up to $300 billion a year, according to the consulting firm McKinsey & Company, the upside of industrywide analytics to manage a population is considerable.

And, increasingly, providers have the raw data they need to feed an analytics system. But it is not as simple or quick as installing electronic health record technology—no small feat in itself for many organizations—and must be accompanied by solid governance and education, according to leading providers.

These providers are using analytics to bring a more intense focus on gaps in care, to discover cost outliers, and to put a magnifying glass on efficiency. But the use of such healthcare analytics has yet to reach maturity.

Early in the process

“Our organization is facing what most of the industry is facing, and that is the need to build a bridge to the future through analytics; so unlike some other industries that may be high users of data and very sophisticated, the healthcare industry is just in a different point,” says Aric Sharp, vice president of the accountable care organization at UnityPoint Health, a West Des Moines, Iowa–based integrated health system with 3,026 licensed beds across 15 hospitals and total operating revenue of $2.7 billion.

“We’re still in the process, as an industry, of going through implementing electronic health records and achieving meaningful use and those types of things. At the same time, with a lot of the new efforts around accountable care organizations, for one of the first times many providers have an opportunity to collect claims data by working with payers,” Sharp says. “We felt it necessary to build a platform where we can mesh together both claims data and data out of our electronic health records, because there’s a lot more that’s able to be learned in that type of an environment. The type of intelligence that we can glean is at a much more informed level than if we’re just dealing with one of those data sets in isolation.”

UnityPoint Health typifies numerous providers, having initiated analytics for its population health initiative only a couple of years ago. “The primary lesson is, this is really difficult, and there’s a lot to learn along the way,” Sharp says. “And yet, we can certainly see that as we continue to enhance the work, there’s more and more benefit with every step. The big learning is that there’s just a lot to be learned, and it’s exciting, because with every step of the process, we are better able to identify opportunities to improve care, and we’re able to become more efficient at this type of work.”

At the heart of population health analytics is the concept of risk stratification: understanding, through various inputs such as claims data, surveys, and EHRs, which members of a given healthcare organization’s customer base represent a level of risk for which intervention offers the greatest possibility of preventing future hospital admissions, reducing readmissions, improving overall health, and lowering costs.

UnityPoint Health selected analytics technology from Explorys, a data spinoff of Cleveland Clinic founded in 2009.

“Explorys is able to pull data from a variety of sources—multiple electronic health records, our own billing systems, claims data from CMS or other payers—and assimilate that all together,” Sharp says. “Explorys is really what sits on top of that and gives us an ability to slice and dice and analyze it and probe it and report quality metrics, identify gaps in care, and in the future even use that to do outreach to patients and do registry-type functions.”

UnityPoint Health still counts the time until the big payoff in years. “We’re not yet ready to say that it has an impact on our global per-member per-month spent,” says Vice President of Operations Kathleen Cunningham. “It will, but we are so early in our innovation that some of our results are really based on the pilot type of innovation programs that we’re working on.”

Starting with employee populations

In many healthcare systems, population health analytics success stories are just beginning to emerge, but some providers have used their own employee populations as a proof of concept for the effectiveness of the effort.

For the past 11 years, employees of Adventist HealthCare—a nonprofit network based in Gaithersburg, Md., with three acute care and three specialty hospitals, 6,263 employees, and 2012 revenue of $726 million—have been managed for risk by the self-insured provider.

“It got started with the idea that a healthier population is going to be a more effective employee population, and it’s going to also be a lower-cost population,” says Bill Robertson, president and CEO of Adventist HealthCare.

 

A decade ago, Adventist started working with InforMed Healthcare Solutions, since acquired by Conifer Health Solutions, to use InforMed’s set of data warehouse tools to improve its health plan design and determine where interventions were needed, Robertson says. Adventist and InforMed worked collaboratively to develop those tools and restructure the Adventist workflow to ramp up the effectiveness of the population health program.

As a result of population analytics, as well as other measures such as discouraging tobacco use and encouraging use of generic drugs, the inflation rate of Adventist’s employee health plan cost over the past nine years was half the national average, Robertson says.

A key development in the population health initiative came in 2005, when Adventist created personal health nurses as part of a pilot patient-centered medical home to work with the approximately 360 high-risk members of Adventist’s 6,600 employee-based covered lives identified by the InforMed data tools, Robertson says.

In a pilot, Adventist selected 27 of 50 high-risk patients (54%) and was able to move them out of the high-risk pool into moderate or low-risk pools, and it achieved a 35% reduction in the cost of care for that population, he says.

According to Adventist, the pilot project that achieved the 35% reduction did reduce health plan costs by $381,000 among the 27 patients who moved from the high-risk pool. The amount expended to achieve this 35% reduction was only $31,000, so every dollar spent returned approximately $12 in savings.

“It was actually so dramatic that it brought the inflation rate on our health plan to zero in that year,” Robertson says. “We were pretty pleased with that.” Overall, Adventist has saved “tens of millions of dollars” due to employee population health analytics to reshape the program and services for employees, he says.

Adventist then expanded this pilot PCMH to 5% of its employees (roughly 360 people), and continues to see the same kind of positive outcomes, Robertson says. Nurses make up the majority of InforMed users.

Three years ago, Adventist created ACES, which stands for Ambulatory Care EHR Support, an initiative to move its ambulatory physicians to use electronic medical records to expand its capacity to do population-focused care. By the end of 2013, more than 400 physicians will be using the ACES system. “So much of the job is how you integrate care across physicians and across the delivery system,” Robertson says. “When you have one person who’s seeing 15 physicians, but each physician thinks they’re the only one, you end up with different challenges than when you can see everything.”

All physicians who are participating providers in the Adventist HealthCare employee health benefit plan have access to the InforMed tools and analytics. Only a limited number directly access the information because the personal health nurses provide most of the ongoing care management, with the physicians serving more as the team captains, Robertson says.

The next step for Adventist IT is to tie analytics with the employee EHR. “What we’re morphing toward is linking all of this together with HIE infrastructure so that the information that is in the InforMed platform will be available in your EHR platform and vice versa through the information exchange,” Robertson says.

Adventist also created financial incentives that help its physicians spend “all the time it takes” to manage high-risk patients, Robertson says. “With an ACO, you don’t really get paid an incentive until you’ve been successful—at least after the first year you’ve demonstrated that things are working and that they’re [generating] shared savings,” he says. “So we’re still in the process of sorting out how we’ll make sure this infrastructure is utilized actively.”

Detailing the financial incentives, Robertson says the primary care physicians who participate in the patient-centered medical homes receive additional compensation, such as a monthly retainer or hourly incentive to compensate them for the additional time that is necessary to care for the high-risk patients in the PCMH.

Recent headlines have highlighted some fallout from the Pioneer ACO program. Fifteen charter members dropped out of the program after finding inadequate return on investment or improvement from their ACO initiative. To Robertson, this just highlights the importance of population health analytics in achieving ACO success. Had Adventist focused on no-risk or low-risk populations, it might not have achieved nearly the cost savings it had with its own proof of concept by targeting the high-risk pool of its self-insured employee-based covered lives, he says.

Now Adventist is forming an ACO for Medicare populations based on this same set of tools to track high-risk members of those populations. As time goes on, commercial-payer populations are also in Adventist’s sights. “We have a couple of pilots, like an apartment building that has a very large population of higher-risk individuals that we’re providing those types of services to, and it’s interesting to see when you focus on it what you achieve in terms of reduced consumption of healthcare services and increased health status,” Robertson says.

 

Leading the way to better patient care

At Virtua Health, population health analytics from Alere Analytics is being implemented to determine the highest-risk patients from a cohort of 12,000 attributed Medicare lives, says James Gamble, MD, chief medical information officer of the four-hospital, 885-staffed-bed integrated delivery network headquartered in Marlton, N.J.

Virtua became an ACO on January 1 and is preparing to add another 14,000 covered lives with a commercial insurer, says Alfred Campanella, Virtua’s executive vice president of strategic business growth and analytics.

“There are lots of different scenarios where action is needed to prevent an admission or to prevent a condition from getting worse,” Campanella says. Virtua is working with Alere to publish its alert lists via a Microsoft Dynamics customer relationship management platform. “That allows care nurses to take advantage of our Microsoft products like email and word processing,” he adds.

Virtua uses RNs to provide close case management of the high-risk population. Meanwhile, 80 Virtua-employed primary care doctors are kept updated via the workflow into the system’s electronic health record software. “That way that doctor doesn’t have to leave their EMR or jump around to see where things are going,” Campanella says.

“Our initial focus,” Gamble explains, “will be on these high-risk patients, so as we see it, these case managers’ day-to-day job will be: They’ll have a patient load, they will have care plans, they will have activities assigned to them for these patients.”

But the physician does not need to be the primary manager.

“As long as patients are following care plans, which are developed and approved by the providers, then the nurses will be managing them,” Gamble says. “Their communication will be more as updates. When an alert arises that the patient is at risk or in trouble, then obviously the nurse would directly communicate with the physician to try to intervene at any early stage before the patient’s health deteriorates or the patient ends up in the emergency room of the hospital.”

“What we’re seeing now is a more intense focus to try to fix those gaps in care and to identify patients who are at high risk for hospitalization or readmission or who need special attention,” Campanella says. “Technology gives you a greater magnifying glass in many respects for seeing the barriers to care and for creating efficiencies in care delivery. While all the analysis is not complete, early results for clinical and financial savings are promising.”

Support from top leadership has been crucial to Virtua’s transformational pivot toward analytics. “This whole idea of care coordination was approved at the board of trustees level,” Campanella says. “We’ve had tremendous support from our CEO, Richard Miller. One of our senior vice presidents, Stephen Kolesk, MD, doubles as the president of this subsidiary that is the ACO. He has a title of senior vice president for clinical integration, so it’s very tightly integrated with the physicians.”

Technical design of the Virtua analytics solution is close to completion. Parts of it will deploy before the end of 2013, and other parts will roll out in the first quarter of 2014, says Campanella. Also part of the project are an existing health information exchange and a new patient portal built on top of the HIE, he adds.

“Innovation does require some experimentation and risk,” Campanella says. “The ones who are leaders are taking on some risk and putting some investment in without fully understanding the full picture, but that’s what makes them leaders.

“It’s now the right way to care for patients, to have this high touch, high visibility into all the different domains of their care and the handoffs between those domains, and so even if the ACO concept from a regulatory standpoint goes away, it’s still the right way to care for patients,”
Campanella says.

Outside the hospital walls

Organizations beyond postacute hospitals are also engaging healthcare in a variety of ways that have broad implications for how analytics will be deployed in healthcare across the United States.

Brentwood, Tenn.–based Brookdale Senior Living owns and operates about 650 senior living communities in 36 states. In 2012, Brookdale, through a partnership with the University of North Texas Health Science Center and Florida Atlantic University, received $2.8 million of a $7.3 million Centers for Medicare & Medicaid Services Health Innovations Challenge grant for population health management. The program expects to save more than $9 million over a three-year period.

Initially, Brookdale is focusing on population health at 27 communities in Texas and Florida, but by the end of the three-year grant, it will involve 67 communities, says Kevin O’Neil, MD, chief medical officer of the organization.

The CMS grant sets a goal for Brookdale of reducing avoidable hospital readmissions by 11%, O’Neil says. “We know we’re going to be focusing on certain quality metrics in addition to readmissions,” he says. “We’ll focus on dehydration rates, as well as new incidents of pressure ulcers, some of the major problem areas in geriatric care, and then, based on the data that we receive from the analytics tool, it’ll help guide our quality improvement teams in terms of the type of improvement efforts that need to be initiated.”

 

A variety of tools exist to help stratify risk. Some tools place members of a population on a scatter plot to make the identification of outliers easier. Other tools organize a population into patient registries to track various diseases and treatments. Still other tools use input gathered from patient surveys. A recent study, however, reported that many of those tools had not performed very well.

At St. David’s Health System in Austin, which is working with Brookdale on the challenge grant, 60% of readmissions recently were measured as coming from low-risk groups. “To me [this] means either that people hadn’t been stratified properly, or that they were being sent home when they probably did need some kind of service or follow-up,” O’Neil says.

The biggest hurdle in O’Neil’s experience with population health analytics has been engaging with the hospital C-suite to craft the business associate agreements necessary to manage populations. “Once we’ve developed a relationship with one entity and had success, it’s much easier to engage other entities within that system.”

In dealing with the two universities, O’Neil says, “We had to resolve some issues related to intellectual property to incorporate INTERACT into electronic information systems,” he says. INTERACT is an acronym for Interventions to Reduce Acute Care Transfers, a free quality improvement program for which FAU holds the trademark and copyright. “This has been resolved through a licensing agreement—Loopback [a Dallas-based analytics platform vendor] also has a licensing agreement with FAU to bake INTERACT tools into software programs.”

Both Brookdale and its hospital partners are using a common population health analysis dashboard and software provided by Loopback Analytics. “As a geriatrician, this is the most exciting time in my career, because I’ve always felt that fee-for-service medicine was the bane of good geriatric care because it rewarded volume rather than quality,” O’Neil says. “Having that near-real-time data is really going to be extremely helpful to us.”

Analytics and meaningful use

Analytics tools produce the patient registries that identify gaps in care, not just to meet ACO objectives, but also to meet the requirements of meaningful use stage 2, which takes effect in 2014, says Gregory Spencer, MD, a practicing general internist and chief medical officer at Crystal Run Healthcare, a multispecialty practice with more than 300 physicians based in Middletown, N.Y.

“There are frequently registry functions within EHRs, but the EHR is set up at the patient level,” Spencer says. “It’s not optimized for reporting groups of patients, so to kind of get that rollup, you have to have another layer on top of that to gather it up.”

Thus, some sort of aggregator function is needed. “Usually that is not something that many EMRs do well,” Spencer says. “Registries are mostly condition- or disease-specific lists of patients who satisfy a certain criteria: diabetics, patients with vascular disease, kids with asthma. Care gaps look at all patients who have not had a certain recommended service. There is overlap with the registries, since a list of patients due for their colonoscopy is a kind of registry that needs to be ‘worked’ to get those patients compliant.”

Like numerous other healthcare organizations, Crystal Run’s first foray into population health analytics employed Microsoft Excel spreadsheets.

“The basics can be done with available tools,” Spencer says. “People shouldn’t wait for the killer app that’s out there that’s fancy and has a slick user interface. You can really do a lot with what you have, probably immediately.”

Since 1999, however, Crystal Run has incrementally left Excel behind and built population health analytics reporting tools on top of its NextGen electronic health record software, Spencer says. Crystal Run also adopted the Crimson Population Risk Management service from the Advisory Board Company, which incorporates technology from Milliman Inc. on the back end, he says.

Like other providers, Crystal Run saw the shift coming from fee-for-service to accountable care and took early opportunities to get its hands on claims data and learn how to work with it, Spencer says.

Other resources offering insight to accountable care analytics were the Group Practice Improvement Network and the American Medical Group Association, where Spencer has been able to network with peers who have been pursuing population health analytics longer than Crystal Run has.

The Crystal Run practice, formed in 1996, grew out of a single-specialty oncology practice and today has 1,700 employees. It is designated by the NCQA as a level 3 patient-centered medical home, and in 2012, Crystal Run became one of the first 27 Medicare Shared Savings ACOs.

Analytics have revealed “a lot of surprises at who you think has been getting most of their care from you,” he says. Snowbirds—typically someone from the Northeast, Midwest, or Pacific Northwest who spends substantial time in warmer states during the winter—are receiving significant amounts of care that had been outside of Crystal Run’s knowledge.

But with Medicare claims data examined through its analytics services, Crystal Run has had its eyes opened to previously unobserved cost centers. For instance, the No. 1 biller of pathology services for a 10,000-patient Crystal Run cohort was discovered to be a local dermatologist.

“What it’s all about is improving quality and eliminating waste,” Spencer says. “That waste is [in] tests that aren’t really required [and even some] visits that are [being required]. It’s your habit and custom to see people back at a certain frequency, but when you really start thinking about it, do you really need to see somebody back every three months who has stable blood pressure and has been rock solid? Well, probably not. And so you start doing things like that, and it adds up incrementally.”

 

Crystal Run is able to incorporate patients’ outside visits to providers, Spencer says, “but it’s not easy. We require source documentation to satisfy measures. For example, we scan outside mammogram results into a directory that we can then report against. We don’t take people’s word for dates. We need to have the document.”

Getting the initial claims data from CMS took three months, and then it takes another three or six months’ worth of that data for it to become actionable, Spencer says.

Claims data on any one patient is also plagued by incurred but not reported claims. Until IBNR claims get processed through Medicare or other payers, a true picture of a patient’s treatment is incomplete.

In light of this, it’s important for all concerned to have realistic expectations of what population health analytics can achieve and when, Spencer says.

“Cost is a practical concern we all face in our day-to-day lives,” he says. “You get more for more money, but as in all things, you have to be prudent. I don’t know how you will be able to do business in the very near future without using some form of analytics. How will your quality measures be good enough to meet the ‘gates’ required for contracts? How will you know where you are or if you can grow and how? It has cost a lot of money—money that’s been spent over a long period of time. The cost is into the low millions.

“That said,” Spencer adds, “we are able to take advantage of newer payment models that reward us not just for healthcare, but outcomes. We can potentially get paid for not doing anything—the PMPM that can be negotiated when you show you are doing a good job managing a population of patients.”

Analytics in the ambulatory practice

Gastroenterologist Tom M. Deas Jr., MD, practices as part of North Texas Specialty Physicians based in Fort Worth, an independent physician association comprising nearly 600 family and specialty doctors. NTSP has its own health plan and has been managing Medicare patients at risk for several years.

NTSP provided initial funding for a population health analytics firm, Sandlot Solutions, which has now been spun out as a separate company, although NTSP remains a part owner and Deas also serves as Sandlot’s chief medical officer. NTSP uses Sandlot’s analytics software to manage 80,000 at-risk lives, Deas says.

“Without some of the information technology to identify those patients based on their illnesses, comorbid illnesses, their severity of illness, who their physicians are, where they’ve been going to get their care, and being able to manage the whole spectrum of the care, you’re at a serious disadvantage,” Deas says.

Sandlot’s technology combines claims and clinical data into a robust patient data warehouse that helps meet some of the quality measures required to be an ACO, says Deas. “With the ACO, no matter how much money you save, you don’t get a dime of it if you haven’t met all the quality measures, so if we fall short in that area, it’s economically not good and it’s not good for the patients.”

By default, all Pioneer ACOs received three years of Medicare claims data. Getting the data into the warehouse requires overcoming some well-known healthcare IT issues, such as reconciling that claims data with an enterprise master-patient index, eliminating duplicates, and general patient-matching issues, Deas notes.

Once that was done, NTSP could concentrate on using Sandlot’s analytics to spot and eliminate wasteful services, as such home visits for patients lacking a medical necessity for such visits, Deas says. Analytics-driven interventions can manage a few hundred overutilizers of services as outpatients, focusing care management on them, he adds.

After a year’s effort, NTSP has bent its cost curve through these efforts to the tune of $50 per member per month, Deas says. “Now we’re not completely there,” he cautions. “It’s an incremental process, because you’re not only doing management, but you’re changing behaviors also. You’re trying to get patients aligned with the primary care physician, trying to move them from one source of care that was maybe excessive utilization to another.”

Deas says measuring the ROI of analytics technology remains elusive.

“A lot of people think they just buy an analytics tool and a data warehouse and an HIE and it’ll sit there and solve their problems,” he says. “That is not the case. You have to have human folks using that tool to manage the care of patients, to lower the cost and improve the quality. It’s like me asking you how much more efficient are you with a smartphone than you were five years ago with whatever version of phone you had then. You can’t answer that question. All you know, it’s just one part of what’s happened in the past five years to make you more efficient.”

It no doubt helps that NTSP’s executive director, Karen van Wagner, has a PhD in statistics, giving the organization added expertise to quantify results as they emerge.

Analytics technology is just beginning to make its impact felt in population health management. Careful consideration of products, objectives, workflows, and business conditions will steer providers through potential pitfalls, but the effort is considerable and the challenge to healthcare leadership is ongoing.

“Among the things that made these changes successful is an IT infrastructure that supports population health management and care management,” Deas says. “We still have to throw a fair amount of resources—human resources—at it to make it work.”

Reprint HLR1113-2

This article appears in the November issue of HealthLeaders magazine.

Scott Mace is senior technology editor at HealthLeaders Media. 

complex adaptive systems, data saving lives, facts & data points, healthcare, policy, rapid learning health systems

Preventing medical error

  • diagnostic errors are the most preventable medical mistakes
  • Automation is part of the solution – sifting through medical records to look for potential bad calls, or to prompt doctors to follow up on red-flag test results.
  • Another component is devices and tests that help doctors identify diseases and conditions more accurately
  • online services that give doctors suggestions when they aren’t sure what they’re dealing with
  • changing medical culture is another approach

Source: http://online.wsj.com/news/articles/SB10001424052702304402104579151232421802264

The Biggest Mistake Doctors Make

Misdiagnoses are harmful and costly. But they’re often preventable

A patient with abdominal pain dies from a ruptured appendix after a doctor fails to do a complete physical exam. A biopsy comes back positive for prostate cancer, but no one follows up when the lab result gets misplaced. A child’s fever and rash are diagnosed as a viral illness, but they turn out to be a much more serious case of bacterial meningitis.

Such devastating errors lead to permanent damage or death for as many as 160,000 patients each year, according to researchers at Johns Hopkins University. Not only are diagnostic problems more common than other medical mistakes—and more likely to harm patients—but they’re also the leading cause of malpractice claims, accounting for 35% of nearly $39 billion in payouts in the U.S. from 1986 to 2010, measured in 2011 dollars, according to Johns Hopkins.

The good news is that diagnostic errors are more likely to be preventable than other medical mistakes. And now health-care providers are turning to a number of innovative strategies to fix the complex web of errors, biases and oversights that stymie the quest for the right diagnosis.

Part of the solution is automation—using computers to sift through medical records to look for potential bad calls, or to prompt doctors to follow up on red-flag test results. Another component is devices and tests that help doctors identify diseases and conditions more accurately, and online services that give doctors suggestions when they aren’t sure what they’re dealing with.

twisted_stethescope

Finally, there’s a push to change the very culture of medicine. Doctors are being trained not to latch onto one diagnosis and stick with it no matter what. Instead, they’re being taught to keep an open mind when confronted with conflicting evidence and opinion.

“Diagnostic error is probably the biggest patient-safety issue we face in health care, and it is finally getting on the radar of the patient quality and safety movement,” says Mark Graber, a longtime Veterans Administration physician and a fellow at the nonprofit research group RTI International.

Big Efforts Under Way

The effort will get a big boost under the new health-care law, which requires multiple providers to coordinate care—and help prevent key information like test results from slipping through the cracks and make sure that patients follow through with referrals to specialists.

There are other large-scale efforts in the works. The Institute of Medicine, a federal advisory body, has agreed to undertake a $1 million study of the impact of diagnostic errors on health care in the U.S.

In addition, the Society to Improve Diagnosis in Medicine, which Dr. Graber founded two years ago, is working with health-care accreditation groups and safety organizations to develop methods to identify and measure diagnostic errors, which often aren’t revealed unless there is a lawsuit. In addition, it’s developing a medical-school curriculum to help trainees improve diagnostic skills and assess their competency.

 

Robert Wachter, associate chairman of the department of medicine at the University of California, San Francisco, says defining and measuring diagnostic errors is an important step. “Right now, none of the incentives for improvement in health care are based on whether the doctor made the correct diagnosis,” Dr. Wachter says. But equally important, he adds, “we need to nurture bottom-up innovation.”

That’s already happening. Large health-care systems are mining their electronic records for missed signals. At the Southern California Permanente Medical Group, part of managed-care giant Kaiser Permanente, a “Safety Net” program periodically surveys its database of 3.6 million members to catch lab results and other data that might fall through the cracks.

In one of the first uses of the system, a case manager reviewed 8,076 patients with abnormal PSA test results for prostate cancer, and more than 2,200 patients had follow-up biopsies. From 2006 to 2009, 745 cancers were diagnosed among those patients—and Kaiser had no malpractice claims related to missed PSA tests.

The program is also being used to find patients with undiagnosed kidney disease, which is often found via an abnormal test result for creatinine, which should be repeated within 90 days. From 2007 to 2012, the system found 7,218 lab orders placed for patients with an abnormal test that had not been repeated. Of those, 3,465 were repeated within 90 days of a notice to patients that they needed a repeat test, and 1,768 showed abnormal results. The majority, 1,624, turned out to be new cases of the disease.

Michael Kanter, regional medical director of quality and clinical analysis, says the system enables clinicians to go back “as far as is feasible to find all of the errors that we can and fix them.”

Because the disease is slow moving, Dr. Kanter says, people with a five-year-old undiagnosed case may not have been harmed. Likewise, with many early prostate cancers, “in many of these cases it doesn’t mean harm would have reached the patient,” he says. “But we don’t want patients not to have the information they should have had through some kind of lapse in the system.”

Dealing With the Flood

Electronic records aren’t a panacea, of course, and can even lead to information overload. In a survey of Veterans Administration primary-care practitioners reported last March in JAMA Internal Medicine, more than two-thirds reported receiving more patient-care-related alerts than they could effectively manage—making it possible for them to miss abnormal test results.

Some researchers suggest the best solution isn’t to flood doctors with information but to provide a second set of eyes to find things they may have missed.

The focus now is preventing dangerous delays in follow-ups of abnormal test results. In a pilot program, researchers at the Houston VA developed “trigger” queries—a set of rules—to electronically identify medical records of patients with potential delays in prostate and colorectal cancer evaluation and diagnosis. Records included charts that had no documented follow-up for abnormal findings suspicious for cancer after a certain period, according to the research team’s leader, Hardeep Singh, chief of health policy and quality at Michael E. DeBakey VA Medical Center in Houston and an assistant professor of medicine at Baylor College of Medicine.

The queries were run on nearly 600,000 records of patients seen at one VA facility in 2009 and 2010. Dr. Singh says the use of triggers, which helped find abnormal PSA tests and positive fecal occult blood tests, could detect an estimated 1,048 instances of delayed or missed follow-up of abnormal findings annually and 47 high-grade cancers.

The VA has funded a randomized trial to test whether an automated surveillance system of triggers can improve timely diagnosis and follow-up for five common cancers.

“This program is like finding needles in a haystack, and we use information technology to make the haystack smaller and smaller so it’s easier to find the needles,” Dr. Singh says.

More health-care systems are also turning to electronic decision-support programs that help doctors rank possible diagnoses by likelihood based on symptoms and notes in the medical record. In a study of one such system, called Isabel, researchers led by Dr. Graber found that it provided the correct diagnosis 96% of the time when key clinical features from 50 challenging cases reported in the New England Journal of Medicine were entered into the system. The American Board of Internal Medicine is studying how Isabel could be linked to assessments of physician skill and knowledge.

Another system, DXplain, developed at Massachusetts General Hospital in Boston, was shown in a study last year to significantly improve diagnostic accuracy among first-year medical residents.

Edward Hoffer, associate clinical professor at Harvard and senior computer scientist at Mass General who leads the DXplain program, says the aim now is to have DXplain “push” diagnostic suggestions to physicians through an electronic-medical-records system rather than requiring doctors to initiate a query, which some are still reluctant to do. “We have to focus our attention on dealing with situations where doctors think they know what the diagnosis is, but they don’t,” Dr. Hoffer says.

Other Avenues

New devices also hold promise for confirming a diagnosis and avoiding unnecessary tests. A number of companies are rushing to provide aids such as portable diagnostic equipment and lab tests that can analyze tiny samples of blood and other bodily fluids quickly to detect disease.

Consider MelaFind, which came to market in the U.S. in 2011. The device allows dermatologists to noninvasively examine moles as deep as 2.5 millimeters beneath the surface to gauge the level of “disorganization,” an indicator of irregular growth patterns that are a sign of melanoma, among the deadliest cancers.

New York dermatologist Macrene Alexiades-Armenakas says she uses MelaFind to confirm that a mole is to be removed and prioritize the level of disorganization in multiple abnormal moles. In some cases, when another doctor or the patient has been concerned about a mole, MelaFind supported “clinical diagnosis of a benign mole, thereby sparing them a biopsy,” she says.

But such devices will never replace a thorough physical exam with a trained eye and careful follow-up, says Dr. Alexiades-Armenakas: “These diagnostic tools are aids to increase our accuracy and adjuncts to good physical diagnosis, not a substitute.”

Some efforts to cut down on errors take a different route altogether—and try to improve diagnoses by improving communication.

For instance, there’s a push to get patients more engaged in the diagnostic process, by encouraging them to speak up about their symptoms and ask the doctor, “What else could this be?” At Kaiser Permanente, a pilot program provides patients with a pamphlet that encourages them to think about and write down their symptoms and what concerns or fears they have, encouraging them to ask specific questions to be sure they understand their diagnosis and the next steps they must take.

Medical schools, meanwhile, are teaching doctors to be more receptive to patient input and avoid “anchoring,” the habit of focusing on one diagnosis and excluding other possible scenarios, and “premature closure,” not even considering the correct diagnosis as a possibility.

The Critical Thinking program at Dalhousie University in Halifax, Nova Scotia, established last year, aims to help trainees step back and examine how biases may affect their thinking. Developed by Pat Croskerry, a physician known for his research on the role of cognitive error in diagnosis, it uses a list of 50 different types of bias that may lead to diagnostic error.

The program is being integrated throughout four years of the medical school. Students study cases such as a psychiatric patient with shortness of breath who was assumed to be merely having an anxiety attack; doctors overlooked that she was a smoker on birth-control pills, a risk for the blood clot that later traveled to her lung and killed her.

“If we can teach physicians how to think more critically,” Dr. Croskerry says, “they would be more effective in delivering good care and arriving at the right diagnosis.”

Ms. Landro is an assistant managing editor for The Wall Street Journal and writes the paper’s Informed Patient column. She can be reached at laura.landro@wsj.com.

cool, data saving lives, entrepreneurship, healthcare, healthy habits, meaning, nutrition, policy, politics, quantified self, rapid learning health systems, research methodology, storytelling

Chronic Disease Fear Factor Ageing Messaging

Governments won’t be able to afford you if you are over 70 and can’t work
You will need to be productive
The current health market can only extend your life, but not your productive life
The new health system will have to do both if we are to preserve our standard of living
Sure, people will need to die sometime, but it’s the when, how and why they die that needs to evolve
This health system aims to deliver on this
Australia is well positioned to lead the world on this
Excitement

data saving lives, facts & data points, healthcare, rapid learning health systems, technology

IBM Watson in Healthcare

What makes you sick?

Chronic health conditions impact the lives of billions of people around the world each year.

Chronic illness accounts for approximately 60% of deaths globally each year.

World population: 6.8 billion. 2 billion people worldwide struggle with chronic illnesses like cancer, heart disease and diabetes.

Early and accurate diagnosis has the potential to improve patient success rates, but it can be difficult to establish.

Medical knowledge is growing more quickly than doctors can keep up with.

In the U.S. alone, up to 15% of medical diagnoses are inaccurate or incomplete.

Digitized medicine in North America alone will grow 400% by 2015 —reaching a total of 14,000 terabytes of data, or 7,500 times the data in all U.S. libraries combined.

To give physicians better insight to help improve patient outcomes, WellPoint is pioneering the use of DeepQA technology—otherwise known as IBM Watson—in healthcare.

Imagine a patient describing her symptoms to a physician who has immediate access to Watson through his laptop.

  1. Based on the symptoms described, Watson provides probabilities for five possible diagnoses.
  2. Watson then considers explicitly absent symptoms to reassess these probabilities.
  3. Correlating the symptoms with family and patient histories, Watson is able to refine the hypotheses further.
  4. The process is repeated with a focus on the patient’s current medications.
  5. Final probabilities are determined, and the physician moves on to testing.

Every patient represents a wide spectrum of variables.

Symptoms

  • Fever
  • Dizziness
  • Abdominal pain
  • Back pain
  • Cough

Family history

  • Diabetes
  • Breast cancer
  • Colorectal cancer
  • Coagulation disorders
  • Grave’s Disease

Patient history

  • Hypertension
  • Hyperlipidemia
  • Hypothyroidism
  • Frequent urinary tract infection
  • Smoking

Clinical findings

  • Blood pressure
  • Heart rate
  • Restoration rate
  • Temperature
  • Pain score

Medications

  • Pravastatin
  • – Lasix
  • Aspirin
  • Chemotherapy
  • Antiemetics

Watson: An expert diagnostic system

This groundbreaking system can pore though the equivalent of 200 million pages of medical data and formulate a response in less than 3 seconds, enabling healthcare professionals to make more informed decisions more quickly than ever before.

Natural language processing – Breaks down the communication barrier between humans and computers.

Hypothesis generation – Offers various probabilities rather than attempting a single “right” answer.

Adaptation and learning – Builds knowledge iteratively over time, in much the same way that humans learn.

Correlated patient information

Possible conditions

  • Renal failure
  • UTI
  • Influenza
  • Esophagitis
  • Diabetes
  • Stage 1 lung cancer

WellPoint is using Watson to help physicians become better at what they do — delivering improved care more quickly and confidently than ever before. The potential of Watson doesn’t end there. The same capabilities hold enormous promise for financial services, transportation and more.

data saving lives, entrepreneurship, healthcare, healthy habits, nutrition, policy, politics, rapid learning health systems, research methodology, technology

Katz slam dunks….

  • Used the Harvard Nurses Health Study to develop an algorithm for food healthiness as determined by health outcomes from the study – a GPS for nutrition – CLEVER!
  • Offered to do this with Government in the early 2000s but was knocked back
  • Developed a proprietary algorithm called ONQI, owned by NuVal
  • Choosing higher scoring foods correlates with a lower risk of dying prematurely.
  • “The very government agencies that regulate the food supply are extensively entangled with the entities producing our food, from farm to factory. In comparison, we mere eaters of food have very little clout. The government may be just a little too conflicted on the topic of food to be in the business of putting the truth, the whole truth and nothing but the truth on at-a-glance display.
    Certainly the big food manufacturers, the makers of glow-in-the-dark snackattackables, should NOT be in the business of nutrition guidance whatever their inclination. That approach makes the fox look like a highly qualified security officer for the henhouse.
    Which leaves independent nutrition, and public health experts and private sector innovation. And here we are.
    Private-sector innovation often involves intellectual property, trade secrets and patent applications. It involves some entity making an investment and wanting a return. That is all true of NuVal, for better or worse. It wasn’t my plan – it was just the only way to get this empowering system into the hands of shoppers. Of note, the ONQI remains under the independent control of scientists, and not the business.”
  • This is a terrific strategy – worthy of emulation.

Source: http://health.usnews.com/health-news/blogs/eat-run/2013/06/11/nutrition-guidance-who-needs-to-know-what

Nutrition Guidance: Who Needs to Know What?

  June 11, 2013 

I am writing today about nutrition guidance and who needs to know what to make it useful.

Permit me to disclose right away that I am the principal inventor of the Overall Nutritional Quality Index (ONQI) algorithm, used in NuVal – a nutritional guidance system that stratifies foods from 1 to 100 on the basis of overall nutritional quality: the higher the number, the more nutritious the food. As the Chief Science Officer for NuVal, LLC, I am compensated for my continuous and considerable allocations of time and effort. But it was never supposed to be that way – and the reasons why it is are an important part of this story.

As to why this column now, there are two recent provocations. One is our ongoing work to complete the updated algorithm, ONQI 2.0, and the window that provides into a world of weirder foods than I ever even considered possible. The other is a paper published in the Journal of the Academy of Nutrition and Dietetics a few months back and a more recent exchange of letters related to that article. The article described the advantageous novelties of a nutritional profiling system, such as weighting nutrients for their health effects rather than counting them all the same. But this was less about novelty, and more about NuVal, since the innovations described have long been included in the ONQI.

[See: Debunking Common Nutrition Myths.]

Claims about alleged novelties that were already included in NuVal prompted a letter from my colleagues and me to the journal, which was published along with a response from the original authors. In that response, they acknowledged that the NuVal system included the so-called “novelties” and acknowledged that the ONQI is, to date, the only nutritional profiling system shown to correlate directly with health outcomes. So the real concern, the letter went on, is that the ONQI algorithm is proprietary and the details are not fully in the public domain.

Which brings us back to why NuVal is a private and proprietary system in the first place and whether or not it matters that certain details of the algorithm – which populate 25 pages or so of computer code written in a language called SAS – are not on a billboard. Why isn’t the ONQI public rather than private, and who really needs to know every detail of the algorithm for it to be useful? (All of the nutrients included in it, and the basic approaches used to generate scores, have been published.)

The ONQI, and NuVal, are a private sector innovation because the public sector said: no thanks. In 2003, I was privileged to be a member of a group of 15 academics invited to Washington, D.C. by then-Secretary of Health Tommy Thompson. A Food and Drug Administration task force had been formed to guide efforts related to the control of rampant obesity and diabetes, and we were a part of that effort. We gathered in a conference room with Secretary Thompson, the FDA Commissioner (Mark McClellan) and others, including the surgeon general and the heads of the National Institutes of Health and the Centers for Disease Control and Prevention.

[See: Why Aren’t Americans Healthier?]

We were each given one three-minute turn to offer up one good idea the FDA and other federal agencies might use to help combat the ominoustrends in diabetes and obesity. I used my turn to describe, in essence, the project that later became the ONQI. I suggested that the secretary might convene a totally independent group of top-notch experts in nutrition and public health, perhaps under the auspices of the Institute of Medicine.

The group should have no political or industry entanglements and should be allowed to work for as long as it took to convert the best available nutrition science and knowledge into a guidance system anyone could understand at a glance. I was thinking, in essence, of the equivalent of GPS for nutrition, so that no one trying to identify a better food in any given category would get lost, confused or misled by Madison Avenue.

[See: 10 Things the Food Industry Doesn’t Want You to Know.]

I waited two years for the feds to do something along these lines. When they didn’t, I decided to undertake the project myself, with the backing of Griffin Hospital in Derby, Conn. – a Yale-affiliated, not-for-profit community hospital, which owns the ONQI algorithm to this day. Other than this being a private rather than federal endeavor, all other aspects of the project were just as proposed to the U.S. Secretary of Health. When we completed the algorithm, I offered it again to the FDA. A scientist at the agency recommended a private-sector approach if I hoped to live long enough to see the system do its intended good.

Why didn’t the feds take on the project? We can all conjecture. I suspect it has something to do with the story Marion Nestle told us all in Food Politics, and the stories we routinely hear about the Farm Bill from the likes of Michael Pollan, Mark Bittman and others. The very government agencies that regulate the food supply are extensively entangled with the entities producing our food, from farm to factory. In comparison, we mere eaters of food have very little clout. The government may be just a little too conflicted on the topic of food to be in the business of putting the truth, the whole truth and nothing but the truth on at-a-glance display.

[See: Seeking a More Perfect Food Supply.]

Certainly the big food manufacturers, the makers of glow-in-the-dark snackattackables, should NOT be in the business of nutrition guidance whatever their inclination. That approach makes the fox look like a highly qualified security officer for the henhouse.

Which leaves independent nutrition, and public health experts and private sector innovation. And here we are.

Private-sector innovation often involves intellectual property, trade secrets and patent applications. It involves some entity making an investment and wanting a return. That is all true of NuVal, for better or worse. It wasn’t my plan – it was just the only way to get this empowering system into the hands of shoppers. Of note, the ONQI remains under the independent control of scientists, and not the business.

[See: Mastering the Art of Food Shopping.]

Which leads us back to the second question: Is it a problem for a system like this to be a private-sector innovation? Who, really, needs to know every detail of such an algorithm?

Consider that if you are shopping for a car, you do need to know if it comes with anti-lock brakes or all-wheel drive. But to decide if these are working for you, you don’t need engineering blueprints; you just need to drive in the snow. When shopping for a smartphone, you may want to know if it has GPS. But you don’t need the trigonometry equations on which the GPS is based to determine if it works; you just have to see if it helps you get where you want to go.

Nutrition guidance in general, and NuVal in particular, are just the same. What are the exact formula details? Who cares. We routinely rely on tools based on math and engineering most of us don’t understand – but we don’t need all that input to know if the tools are working for us. We just need the output. We need to be able to use them. People using NuVal have lost more than 100 pounds, and even over 200 pounds. Choosing higher scoring foods correlates with a lower risk of dying prematurely. More than 100,000 scores are on public display in 1,700 supermarkets nationwide. The ONQI is at least as transparent as any car or smartphone or computer.

[See: The No. 1 Skill for Weight Management.]

Let’s acknowledge: If you are reading this on a computer screen, neither of us truly understands the engineering involved in me writing it, using word processing software, attaching it to an email and sending it to my editor at U.S. News & World Report so she could post it in cyberspace, where you found it. But we do know it worked.

We rely on private-sector innovation for a lot of important jobs, and even many that put our safety on the line. The private sector makes our cars and planes. We seem to be comfortable using these without scrutinizing patent applications. The private sector makes our computers, and smartphones and GPS systems, and we can tell whether or not these work, even if we don’t know how.

Why, then, is nutrition guidance different? The answer, I believe, is politics, profits and the inertia of the status quo. We are accustomed to vague nutrition guidance from conflicted sources, and those same sources are apt to imply there is something wrong with private-sector innovation and the intellectual property issues that come along with it. But if those issues don’t undermine the cars, and planes and navigation systems that get us from city to city and coast to coast, it’s not at all clear why they should be a problem when navigating among choices in a supermarket aisle.

[See: The Government’s MyPlate Celebrates Second Birthday.]

As a scientist, and not a businessperson, my preference would be to put the ONQI on a billboard for all the good it would do. But on this, I must defer to the businesspeople who have made the relevant investments and are entitled to safeguard potential returns. As for the scrutiny that all advanced systems should get, the ONQI has been shared with scientists at leading universities and health agencies around the world – but for private assessment and use rather than public display. Others like them who want to review the program need only ask.

We should all care that the military-industrial establishment seems opposed to putting the blunt truth about nutritional quality, as best we know it, on at-a-glance display. We should care that federal authorities responsible for nutrition guidance are also responsible, if only indirectly, for food politics and supply-side profits. That story may lack novelty. It may be old news. But it is nonetheless something everyone who eats does need to know – engineering blueprints not required.

data saving lives, entrepreneurship, facts & data points, healthcare, quantified self, rapid learning health systems, technology

Medical Body Area Network

  • The FCC has proposed the allocation of spectrum for Medical Body Area Network (MBAN) devices.
  • Deloitte expects the wireless health device market to triple in the next few years

From: http://www.fool.com/investing/general/2013/11/17/3-technologies-that-will-change-the-face-of-medici.aspx#!

3. Wireless body monitoring
We need only to listen to the words of FCC chairman Julius Genachowski to get a feel for the potential for wireless body monitoring. Genachowski noted last year that “a monitored hospital patient has a 48% chance of surviving a cardiac arrest,” compared with only 6% for an unmonitored patient.

With the tremendous opportunity for improving health care in mind, the FCC proposed allocating spectrum for Medical Body Area Network, or MBAN, devices. Such devices will record vital signs and other important physical information through sensors attached to a person’s body, with the data transmitted to a local wireless hub. The information can then be monitored remotely by clinical professionals, with alerts sent to let these experts know when medical intervention could be needed.

GE Healthcare (NYSE: GE  ) is one company already developing MBAN devices. The giant company plans to introduce technology using sensors that monitor heart and breathing rates, temperature, and pulse oximetry within the next few years. Deloitte predicts that the wireless body monitoring market could more than triple in just the next couple of years. Within the next decade, this technology could be key in helping control overall medical costs.

complex adaptive systems, data saving lives, entrepreneurship, healthcare, healthy habits, musings, nutrition, policy, quantified self, rapid learning health systems

Because in health, less is more…

When we look back at contemporary health systems 50 years from now, we will consider them to be an technologically indulgent folly of grand proportions, driven by an imperative to deliver more and more complex care in order to justify higher and higher costs.

In a fee-for-service context, elaborate technologies justify higher costs. An elective angiogram costs $25,000. If this had to be paid by individuals, there would be no interest in conducting them with the frequency that they are performed today.

Perhaps this is why Singapore, with its health savings accounts with health costing around 4% of GDP (achieving the same high outcomes of Australia), lacks the excesses of more universal health systems?

The use of bariatric surgery for obesity is perhaps the most egregious example of this phenomenon. A AU$20,000 – 30,000 procedure is now introducing moral hazard that will undermine attempts to introduce behavioural and lifestyle change i.e. “Why bother changing my lifestyle when I can simply get a lap band to fix me later?”

Pharmaceutical companies are also using this play book with the introduction of their new, highly-specialised, so-called “biologics” to the market, particularly in the cancer area. They are often protein based and extremely difficult to manufacture, but are also very targeted. Funders are responding to this threat with value-based payment schemes where by the drug company only gets paid if the treatment succeeds.

Current health market settings establish this perverse incentive. Moves to value/outcomes-based care will remedy these perversities, providing incentives for activities that reduce care costs. In such an environment, the cheapest interventions also become the most profitable.

Home delivered broccoli instead of lap-bands.

CBT SMS’s instead of SSRIs and psychotherapy.

A rapid learning health system instead of a profit yearning sickness market.

 

data saving lives, entrepreneurship, quantified self, rapid learning health systems, technology

Extreme baby monitoring

  • baby monitoring doesn’t seem to address anything except parental free floating anxiety
  • no insights have yet been gleaned on baby (or parental) behaviour

Source: http://www.fastcompany.com/3021601/innovation-agents/tracked-since-birth-the-pros-and-cons-of-extreme-baby-monitoring

TRACKED SINCE BIRTH: THE RISE OF EXTREME BABY MONITORING

DOES TRACKING A BABY’S EVERY MOVEMENT, CRY, AND WET DIAPER MEAN HAPPIER PARENTS AND HEALTHIER INFANTS, OR ARE WE TURNING OUR KIDS INTO TAMAGOTCHIS FOR NO REASON?

 
Elle Lucero has been tracked since birth.

For the first 10 months of her life, her mother, Yasmin, kept detailed records of Elle’s sleep patterns, feedings, and diaper changes, noting the data points with a pencil and paper on a clipboard. A few months in, she digitized the logs, graphed the data, and became a more knowledgeable parent.

“It helped me feel confident,” she told Fast Company.

Elle wasn’t a very good sleeper, even for a baby. The pediatrician told Yasmin she needed to let her daughter “cry it out” until she fell asleep, but that never worked. For the sake of her sanity (and sleep), Yasmin took problem solving into her own hands. She wanted answers: Did she put Elle to bed too early? Too late? Give her too many naps? Parsing data, she thought, would help her figure it out. “That was the kind of stuff we were looking for,” she said.

Unfortunately for the Lucero family’s sleeping habits, Yasmin never found a definitive answer. Per the data, Elle was just fussy.

The results suggested Yasmin couldn’t engineer better naps, as she’d hoped. Just knowing that, however, made her feel better. “If you come to the conclusion that you have no control, then it’s okay to relax and just do whatever is convenient for you at the moment,” she explained. (Of course, many parents come to this conclusion at the moment of birth, without all that tedious data tracking.) But for Lucero, a conclusion–any conclusion at all–was all she wanted.

Many new and sleep-deprived parents crave that peace of mind and would kill for a data set that helped them determine if putting little Emma down an hour earlier would mean a restful night for the whole family. But unlike Yasmin, most people aren’t trained statisticians. Tired moms and dads with no mathematical background aren’t about to write down hundreds of data points, and might not know how to analyze that information anyway. Twenty-two months into Elle’s life, even Yasmin has semi-abandoned the project, and keeps much less rigorous records now.

In the imminent future, though, any curious parent with an iPhone will have access to helpful analytics, thanks to the rise of wearable gadgets for babies. Following the success of self-trackers for grown-ups, like Jawbone and Fitbit, companies likeSproutling, Owlet, and Mimo want to quantify your infants.

Mimo Onesie

These devices connect to a baby via boot, anklet, or onesie, and record his or her heart rate, breathing patterns, temperature, body position, as well as the ambient conditions of the room. They aim to replace baby monitors, which give an incomplete picture of a sleeping child. There’s also the nascent “smart diaper” market, led by Pixie Scientific, which scans dirty diapers for signs of infection.

In addition to alerting parents of any concerning findings, these companies encourage a big-data approach to parenting. By gathering information on your kid’s poop, sleep, and eating schedules, the idea goes, you can engineer a happier, healthier baby. The accompanying app for the Sproutling monitor, for example, looks at patterns specific to your child and its environment to offer insights–the kind that Yasmin craved–that might help the child sleep better. It might find that little Jake naps better in complete dark, for example.

The Sproutling monitor

In theory, all this data will lead to more rested, relaxed parents and healthier kids. As of now, parents do a lot of this in the dark. “There’s no owner’s manual,” Sproutling CEO Chris Bruce told Fast Company. His company hopes to change that. “It’s smart technology that helps raise the parenting IQ.”

When Bruce talks about “parenting IQ,” he doesn’t just mean his customers. Sproutling and its cohorts want to use their arsenals of data to better inform research. “The promise of big data is that we can monitor every single environmental parameter and we can find correlations and detect patterns,” added Bruce, calling big data the “holy grail” of his business. Both Owlet and Sproutling indicated that they will offer up their intel–anonymously!–to researchers so that all future parents can better understand babies.

Parents like Yasmin, who haven’t had a full night of sleep in months, are desperate to have that information. She didn’t want to know average sleep patterns–information available in baby books–she wanted bell curves. Yasmin knew her baby wasn’t normal, but she didn’t know how abnormal and her own analyses couldn’t clarify that, either. “I wasn’t finding the exact data I wanted to see,” Yasmin said, after scouring the Internet for answers.

An aggregation of Yasmins, however, can provide those insights. At least that’s the hope.

What sounds like a lot of progress for parenting also means handing a digital record of your baby over to an iPhone app. Are the benefits worth that?

While these apps could improve infant health by telling a parent the exact right nap or changing time, the app in large part benefits parents. Anxious first time moms and dads who worry about every little movement (or non-movement) can monitor their children more closely than ever. “You see your baby lying there and you don’t see them moving,” Bruce, who has two young daughters, said of his experience with old-school video monitors. “You can’t see them breathing; your first thought is: ‘Oh my God, something is wrong.'”

Unlike a basic $35 baby-monitor, the $250 Owlet bootie and accompanying app can alert parents if anything serious has gone wrong, like if a kid stops breathing, or if his heart stops beating. That means no more unnecessary freakouts for the over-protective and inexperienced dad like Bruce, which leaves more time for him to do other dad things.

But, to an extent, these apps take advantage of parent anxieties. “SIDS is the number one cause of infant death. That’s really scary to parents,” Jordan Monroe, a cofounder of Owlet, told Fast Company. Monroe has no kids, but while talking to friends and friends of friends with babies, he found that to be a common worry.

Those fears don’t come from a place of reality, though. According to the Center for Disease Control, 4,000 infants die each year from Sudden Unexpected Infant Death. Only a fraction of those deaths occur because of “accidental suffocation and strangulation in bed,” according to the CDC report. And even SIDS–which causes about 2,000 deaths a year–might stem from underlying brain issues, according to recent research. Monitoring a child’s breathing with a high-tech bootie won’t cure SIDS.

As anyone who has ever had any contact with a hypochondriac knows, those facts don’t really matter. Parents will continue to worry. And, as we saw with Yasmin, certainty has a lot of value. A certain type of parent, like TechCrunch’s Leean Rao, thinks that $250 for Owlet or $200 for Mimo’s version–Sproutling hasn’t yet announced pricing–is a reasonable price to pay to worry about one less thing. In her review of Sproutling, she writes:

As a relatively new parent myself, I would have loved to be able to use some of the data from a wearable to help determine optimal sleep patterns for my child. I’m not sure if it would have helped my daughter sleep through the night earlier in her development, but to me as a fledgling parent, knowledge is power.

Of course, the dollar amount is only a part of the price parents pay with these apps. They give up their children’s data and possibly privacy. “We’re creating the largest data set of infant health data,” Monroe said–a chilling statement in certain contexts. Trackers could turn around and sell their troves to insurers or be forced to hand them over to the government. The information is also vulnerable to hackers.

These companies say they take security issues seriously. “Security encryption has been designed in our system from the get-go,” said Bruce. Anonymous sharing with researchers is both opt-in and anonymous for Sproutling users. But, even Bruce admits that our cultural acceptance of privacy changes every day. What seems innocuous today might feel invasive tomorrow (or vice versa).

Is that risk worth the stated benefits? At this point, it’s not clear these monitors offer many health solutions. The breathing and sleeping alerts will calm (and draw) a lot of parents. But, none of these companies see that as the “holy grail.” The main sell is the tracking. And what does that do for parents and babies?

Arguably, it means finding those little tweaks that make life easier. But, as Yasmin discovered, sometimes babies fuss just because. Numbers don’t always offer solutions, as technical theorist and staunch critic of the self-quantified movement Evgeny Morozov wrote in his book To Save Everything, Click Here: The Folly of Technological Solutionism. “Self-trackers gain too much respect for the numbers and forget that other ways of telling the story–and generating action out of it–are possible.”

While pediatricians typically ask new parents to chart and report feedings and bowel movements for a few weeks after bringing babies home to make sure all systems are go, obsessive tracking beyond that could get in the way of parenting, some doctors say. “Often, when babies have regained their birthweight and are 10-14 days old, I instruct families to dial the tracking down,” Dr. Wendy Sue Swanson wrote on her blog. She adds:

I want new parents to gain confidence and appreciate the homeostasis with following a baby’s natural routine. Relying only on the numbers may cause parents to miss out on the nearly unspeakable experience of parenting a new baby and all that a baby intimately communicates from the beginning. It’s better to look up at the sky to know if it’s raining than to consult the weather report on your iPhone.

After all, do you really want to treat your child like a Tamagotchi?

data saving lives, healthcare, rapid learning health systems, technology

Hammerbacher heads big data at Mt Sinai

  • accountable care is a system in which hospitals are paid to keep people healthy
  • the new economic incentives drive a need for data regarding the population being treated
  • Joel Dudley (Director of Informatics at Mount Sinai Medical School) is running diabetic patient data through an algorithm to cluster them according to phenotype and genotype.
  • This work aims to to replace the general guidelines doctors often use in deciding how to treat diabetics and replace them with risk models—powered by genomics, lab tests, billing records, and demographics—making up-to-date predictions about the individual patient a doctor is seeing, not unlike how a Web ad is tailored according to who you are and sites you’ve visited recently.

Source: http://www.technologyreview.com/news/518916/a-hospital-takes-its-own-big-data-medicine/

MIT Technology Review Report: A Cure for Health Care Costs (good infographics)

A Hospital Takes Its Own Big-Data Medicine

The person leading the design of the new computer is Jeff Hammerbacher, a 30-year-old known for being Facebook’s first data scientist. Now Hammerbacher is applying the same data-crunching techniques used to target online advertisements, but this time for a powerful engine that will suck in medical information and spit out predictions that could cut the cost of health care.

With $3 trillion spent annually on health care in the U.S., it could easily be the biggest job for “big data” yet. “We’re going out on a limb—we’re saying this can deliver value to the hospital,” says Hammerbacher.

Mount Sinai has 1,406 beds plus a medical school and treats half a million patients per year. Increasingly, it’s run like an information business: it’s assembled a biobank with 26,735 patient DNA and plasma samples, it finished installing a $120 million electronic medical records system this year, and it has been spending heavily to recruit computing experts like Hammerbacher.

It’s all part of a “monstrously large bet that [data] is going to matter,” says Eric Schadt, the computational biologist who runs Mount Sinai’s Icahn Institute for Genomics and Multiscale Biology, where Hammerbacher is based, and who was himself recruited from the gene sequencing company Pacific Biosciences two years ago.

Mount Sinai hopes data will let it succeed in a health-care system that’s shifting dramatically. Perversely, because hospitals bill by the procedure, they tend to earn more the sicker their patients become. But health-care reform in Washington is pushing hospitals toward a new model, called “accountable care,” in which they will instead be paid to keep people healthy.

Mount Sinai is already part of an experiment that the federal agency overseeing Medicare has organized to test these economic ideas. Last year it joined 250 U.S. doctor’s practices, clinics, and other hospitals in agreeing to track patients more closely. If the medical organizations can cut costs with better results, they’ll share in the savings. If costs go up, they can face penalties.

The new economic incentives, says Schadt, help explain the hospital’s sudden hunger for data, and its heavy spending to hire 150 people during the last year just in the institute he runs. “It’s become ‘Hey, use all your resources and data to better assess the population you are treating,’” he says.

One way Mount Sinai is doing that already is with a computer model where factors like disease, past hospital visits, even race, are used to predict which patients stand the highest chance of returning to the hospital. That model, built using hospital claims data, tells caregivers which chronically ill people need to be showered with follow-up calls and extra help. In a pilot study, the program cut readmissions by half; now the risk score is being used throughout the hospital.

Hammerbacher’s new computing facility is designed to supercharge the discovery of such insights. It will run a version of Hadoop, software that spreads data across many computers and is popular in industries, like e-commerce, that generate large amounts of quick-changing information.

Patient data are slim by comparison, and not very dynamic. Records get added to infrequently—not at all if a patient visits another hospital. That’s a limitation, Hammerbacher says. Yet he hopes big-data technology will be used to search for connections between, say, hospital infections and the DNA of microbes present in an ICU, or to track data streaming in from patients who use at-home monitors.

One person he’ll be working with is Joel Dudley, director of biomedical informatics at Mount Sinai’s medical school. Dudley has been running information gathered on diabetes patients (like blood sugar levels, height, weight, and age) through an algorithm that clusters them into a weblike network of nodes. In “hot spots” where diabetic patients appear similar, he’s then trying to find out if they share genetic attributes. That way DNA information might add to predictions about patients, too.

A goal of this work, which is still unpublished, is to replace the general guidelines doctors often use in deciding how to treat diabetics. Instead, new risk models—powered by genomics, lab tests, billing records, and demographics—could make up-to-date predictions about the individual patient a doctor is seeing, not unlike how a Web ad is tailored according to who you are and sites you’ve visited recently.

That is where the big data comes in. In the future, every patient will be represented by what Dudley calls “large dossier of data.” And before they are treated, or even diagnosed, the goal will be to “compare that to every patient that’s ever walked in the door at Mount Sinai,” he says. “[Then] you can say quantitatively what’s the risk for this person based on all the other patients we’ve seen.”