Links to
Part I Overview, Watson, analytics
Part II digital devices, long term care.
Part III Apps, devices, roadblocks

Behavioral and Population Health; Roadblocks to change

Can mobile apps really improve mental health? Cut the costs of health care? Help professionals track and care for patients? At a day-long conference sponsored  on health systems innovation organized the MIT/Sloan MIT  Initiative for Health Systems Innovation (HSI), experts from a variety of fields attempted to answer those and other questions aimed at furthering a transformation of  the US healthcare system.  Part III of a series about the conference describes apps and devices for behavioral health, personalized and long-distance care. It also discusses new state models to integrate community, health and social systems aimed at tracking and caring for patients and points out that no matter how sophisticated the technology, it is still up to human beings to make it work.

Behavioral Health

Dan Mordecai, MD, National Leader, Mental Health and Wellness at the managed care consortium Kaiser Permanente described:

• Promising mobile apps aimed at helping people prevent or overcome eating disorders, addiction, or suicide; remain on diets or exercise plans; or connect them with treating providers or coaches.
• Wearables that can measure how much people move or perform text and voice analysis to help professionals understand who needs care, months or years before it is needed.
• Predictive analytics to help prevent self-harm

While many of the above technologies have yet to be proven effective, Mordecai said, telehealth technology has been shown to be helpful in supporting and promoting long-distance health care for areas with shortages of medical personnel.  Telehealth may be carried out through videoconferencing, store-and-forward imaging, streaming media, and terrestrial and wireless communication.  

Mordecai also pointed out that with digital advances, “we are moving from individual doctor patient relationships” to a “personalized’” system, which relies increasingly on data, but that “there is a long way to go.”    Mordecai plans to use what he termed “crowd sourcing” to analyze the effectiveness of apps and other new health technologies, based on the electronic health records of Kaiser Permanente’s   nearly 12 million patients.

 

State models and population health
Analysis like that used at Kaiser Permanente is crucial for assessing treatment and cutting costs, but it is more challenging to perform outside of managed care programs, which have access to a vast array of patient records, according to Michael Wilkening, the California Undersecretary for Health and Human Services.  Analysis to records for care funded by government or private insurance is hampered by fragmented  social, health provider and  insurance systems and by legal and technical challenges of sharing patient data among those systems,

The New York State Medicaid director, Jason Helgerson, pointed out that for state Medicaid systems,  which serve mainly low-income populations,  it can be difficult to simply keep track of patients,  much less co-ordinate and evaluate their care or reduce their treatment costs.  As an example, he described a city homeless shelter that serves breakfast and dinner, but not lunch. Hungry residents regularly go to the fire station next door and complain of chest pain; they are taken by ambulance to a hospital emergency room, where they are evaluated, at high cost, given lunch, and then transported back to the homeless shelter in time for dinner.

Medicaid systems in at least several states are working on projects to prevent such situations by better integrating social services with medical and behavioral health care. Some are starting to employ analytics to recommend, monitor and measure the success of treatments, and to pay for performance rather than service.  As a result, Helgerson said, “Medicaid may be in the best position to drive change” in health delivery systems.

Roadblocks to change
Still, as a variety of speakers pointed out, despite the promise of digital innovation, there are many roadblocks to change.   Such roadblocks include: reluctance to replace or augment human decision-making with digital solutions;  complex reimbursement systems  and the need for insurer “buy-in” to pay for new technologies;  disparate stakeholders with different goals;  issues of privacy and security;   the  tendency of legislators and other policymakers to view health problems as individual rather than societal;   failure to address the lack of food and shelter that leads to poor health and expensive repeat hospital visits;  and, last but not least, cost.

In the words of Vocera’s Elizabeth Boehm, regarding systemic change, “it takes more than technology to get it done.“

And, as Restef Levi, of the Sloan School, put it: “Technology is important but…at the end of the day, health is about humans.”

 

LINKS TO:

Part I Overview, Watson, analytics
Part II digital devices, long term care.
Part III Apps, devices, roadblocks

Videotapes and photos of the conference, held November 29, 2017, are available at http://mitsloan.mit.edu/alumni/events/2017-cambridge-health-conference/

–Anita M. Harris

Anita Harris is a writer and communications consultant specializing in health science and technology.

New Cambridge Observer is a publication of the Harris Communications Group, a content and digital marketing firm based in Cambridge, MA.

Imagine this: a city homeless shelter serves breakfast and dinner, but not lunch. Hungry residents regularly go to the fire station next door, complain of chest pain, get taken by  ambulance to the hospital ER where they are evaluated, at high cost, given lunch, and then transported back to the homeless shelter in time for dinner.

Such was a scenario laid out recently at the MIT Sloan School as an example from an inefficient US social and health care system that cannot adequately keep track of patients, manage health of the poor, or control costs.

The scenario was described by Jason Helgerson, Medicaid Director for the New York State Health Department, at a day-long conference entitled “Health Systems Innovation.”  At the November 29 conference,  experts from industry, government, science, medicine and academe laid out some of US health care’s daunting problems, along with new visions and hoped-for solutions —many powered by digital innovation.

In introducing the sessions, which were organized by the MIT Sloan Initiative for Health Systems Innovation (HSI), moderator Jay Levine  pointed out that, today, there is more uncertainty regarding health care than at any time since the enactment of Medicare in 1965—a result of “overwhelming” political turbulence and concerns about health insurance and a tax plan that could upend the health industry and lead to huge, unsustainable losses in health delivery. Levine is retired principal of ECG Management Consultants, Inc.

 Retzef Levi,  an MIT/Sloan management professor whose department hosts HSI, outlined burgeoning health issues that accompany an aging US population. He emphasized the need to “sow seeds now”  for a “visionary, futuristic system”  to prevent disease  and know who is at risk in order to reduce future illness.

Such a system would  integrate local, state, and national systems, medical  and behavioral disciplines, primary,  specialty and community care, Retsef said.  Providers would be paid for performance rather than tasks undertaken. There would be a workforce sufficient to handle the nation’s long term care needs, personalize diagnosis and treatment. The system would make full use of  digital health innovations such as big data, analytics, sophisticated devices and mobile apps without losing the best aspects of human care.

After Levi’s remarks, Levine asked, “ in light of huge current losses in the system, where does the money come from to fix it?”

Panelists at the day long meeting never fully answered that question, but they did lay out a variety of promising digital approaches that could lead toward transformation, as well as roadblocks to change.

Analytics, machine learning and artificial intelligence
In a panel on “Machine Learning in surgery and cancer”, MIT PhD Candidates Jack Dunn and Daisy Zhou described analytic tools, based largely on longitudinal patient records,  that they are developing  to predict how long an individual surgical patient is likely to live, with what quality of life, if certain decisions are made.  Such tools, which evaluate “nuanced “ signals and  make use of “decision trees,” are aimed at helping doctors decide on treatment plans. Under current treatment guidelines, Zhou said, many doctors tend to “overestimate” prognoses, which can diminish patients’ quality of life and increase medical costs.

Dusty Mojumdar, PhD, IBM vice president and chief marketing officer for an IBM artificial intelligence (AI) system that reads, learns, understands and interacts with humans. Named “Watson,” after IBM’s first president, Thomas Watson, the system is now used, in health care, to: combat a major shortage of radiologists; predict whether nodules on individuals’ lungs will become malignant; develop new targets for ALS drugs, predict hypoglycemic events for diabetics; rank treatment plans and options for seven cancers, and match patients to clinical trials.

Artificial intelligence is also  being used to evaluate what one speaker termed an “explosion “of health data—which is reported in some 7000 new health care publications per day, and which doubles every 73 days, according to Mojumdar.  Several massive health systems are employing  artificial intelligence to co-ordinate electronic medical records—using “text analysis” and “pattern matching” to “catalog” patients with similar health conditions in order to evaluate  and predict outcomes of particular treatments.

In September, IBM made a 10-year, $240 million investment to create the MIT–IBM Watson AI Lab to  carry out fundamental AI research aimed at propelling scientific breakthroughs that unlock the potential of AI.

In Part II, I’ll share panelists’ information about a variety of digital devices and methods already in use to help streamline and personalize long-term care and health care delivery.

 

LINKS TO:
Part I Overview, Watson, analytics
Part II digital devices, long term care.
Part III Apps, devices, roadblocks

Videotapes and photos of the conference, held November 29, 2017,  are available at http://mitsloan.mit.edu/alumni/events/2017-cambridge-health-conference/

–Anita M. Harris
Anita Harris is a writer and communications consultant specializing in health, science and technology.

New Cambridge Observer is a publication of the Harris Communications Group, a content, PR and digital marketing firm based in Cambridge, MA.