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April-21-2014

12:32

snomed

The mandated use of SNOMED seemed to sneak up on healthcare providers, ninja-style. Like a ninja, it has the opportunity to silently deliver deadly blows: confusing care coordination efforts, confusing patients, and contributing to adverse risk scoring and underwriting analysis with private payers.

While providers and health IT professionals are accommodating SNOMED into clinical workflows and converting existing relevant data points to SNOMED to comply with CMS mandates, no one seems to be talking about the potential impact on patient care.

The requirement of SNOMED began innocuously enough. To qualify for Meaningful Use Stage 1 Core measures, CMS declared that Eligible Hospitals, Critical Access Hospitals, and Eligible Providers would record patient “problems” as discrete data rather than narrative text (typically, acronyms).

The Medicare and Medicaid EHR Incentive Programs do not specify the use of 1CD-9 or SNOMED-CT® in meeting the measure for this objective. However, the Office of the National Coordinator for Health Information Technology (ONC) has adopted ICD-9 or SNOMED-CT® for the entry of structured data for this measure and made this a requirement for EHR technology to be certified. Therefore, EPs will need to maintainan up-to-date problem list of current and active diagnoses using ICD-9 or SNOMED-CT® as a basis for the entry of structured data into certified EHR technology in order to meet the measure for this objective.

So, in 2013, providers were told there are two standards for recording structured problem data. They can choose ICD-9 or SNOMED-CT®. Since the objective states “list of current and active diagnoses” and they use ICD-9 for diagnose coding, it should be fine to use ICD-9, right? What’s the difference between an ICD code and a SNOMED code for the same ailment?

In Achieve (Meaningful Use Stage 2) Compliance with SNOMED-CT, Brian Levy gave an excellent synopsis:

Problem lists have been around a long time. Historically, and in many outpatient offices and clinics today, physicians have maintained paper problem lists in the front of patient charts that are updated during each encounter. The problem list basically acts as a running record of the major or chronic conditions suffered by the patient.

By contrast, the diagnoses coded within a practice represent the conditions that prompted the services rendered during a visit. In other words, the diagnosis codes describe the ailments that justify billing for the procedures performed. After a visit, a physician typically marks the appropriate ICD-9 codes on a superbill and sends it to the billing staff.

Geraldine Wade of Clinical Informatics Consulting used this simple SNOMED vs. ICD-9 visualization as part of her presentation to Hong Kong’s eHealth Record Office:

snomed2

It seems simple enough: providers should use SNOMED when recording clinical symptom and diagnosis data, which doesn’t impact billing and reimbursement processes. Given time and adequate clinician training on the differences between SNOMED and ICD codes required for the same diagnosis, that process adjustment could be made.

Fast forward to 2014, the CMS issued clarification that the patient problem lists must be recorded only in SNOMED. Also, any patient diagnosis data already recorded in the problem list must resolve to SNOMED with all active problems, whether newly introduced or historic chronic conditions. Eden Ware described the collective provider reaction to this news in SNOMED: What It Is And Why It Was Added To Stage 2 Meaningful Use:

I can hear my provider colleagues screaming now. ‘No! We are already swamped trying to figure out ICD-10! We just want to care for our patients!’” She goes on to assuage their fears, with visions of IT automation to the rescue:

The good news is that the industry hears you, and products are available in the healthcare IT market to facilitate the translation of your problem lists into reportable, standardized SNOMED-CT codes. It will be important to ask your EMR vendors how they are handling this Meaningful Use Stage 2 requirement. Many vendors are utilizing “maps” between ICD-10-CM and SNOMED-CT to ensure this goal can be met. The mapping from the ICD-10 code to SNOMED-CT occurs behind the scenes, and is easily retrievable. However, the provider’s time is not affected and the goal for meeting this core requirement of Meaningful Use Stage 2 is ensured.

Presto! The SNOMED conversion problem is solved. There are a number of mapping tools available to address the linkage between ICD-9 and SNOMED, including the crosswalk provided by the National Institute of Health’s National Library of Medicine, which was created specifically to address the translation required for patient problem lists. EHR vendors are also building conversion features into their products, such as the Cerner Millennium example below, where a single click will instantly convert 40 patients’ ICD-9 code for CAD to the parent SNOMED code for coronary artherosclerosis:

snomed3

One click, 40 patients’ records altered, all future C-CDA clinical documents compliant with Meaningful Use criteria.

But wait… We’re talking about a patient’s clinical diagnosis data.

IT geeks, in conjunction with EMR vendors and systems integrators, are going to be responsible for making an accurate determination of which ICD-9 diagnosis code maps to a SNOMED code? Is there any risk that these conversions might fail?

NLM blithely discloses the difficulty of achieving a perfect match (emphasis mine):

The Map tries to identify as many one-to-one maps as possible, however, due to the differences between the two coding systems, one-to-one maps cannot be found for some ICD-9-CM codes. This difference is usually due to one of two reasons. Firstly, in ICD-9-CM, some codes are “catch-all” codes that encompass heterogeneous diseases or conditions (e.g. pneumonia due to other specified bacteria). These codes, commonly known as “NEC codes” (not elsewhere classified codes), will not have one-to-one maps because of their nature. Secondly, since SNOMED CT is more granular than ICD-9-CM in most disease areas, some ICD-9-CM diseases or conditions are further refined as more specific concepts in SNOMED CT. For such cases, it is not possible to map to a more specific SNOMED CT concept without the input of additional information.

SNOMED is more granular than ICD-9 in most disease areas—not few, not some, but most.

Osteoarthrosis, the most frequently-occurring “unmapped” problem codes in the above image, has 20+ possible applicable SNOMED concepts related to the parent code 396275006 (Osteoarthrosis disorder), indicating more granular information such as site of osteoarthritis, or whether condition is chronic/endemic/degenerative, etc. Asthma has 25 possible derivations of SNOMED parent code 195967001 (Asthma disorder). Depression has close to 30 SNOMED codes describing parent code 35489007 (Depressive disorder), and the list goes on.

Here’s what the one-to-many mapping possibilities could mean to patients (including automated conversion tools):

The purpose of the SNOMED problem list is to inform all providers in the patient’s care continuum of any active or chronic conditions needing assessment and monitoring. With the single-click application of any given SNOMED code to an entire population of patients, it is highly likely that some, if not many, patients will be incorrectly assigned. It is unlikely that the patient will be clinically educated enough to identify, let alone explain, the difference to the network of providers participating in his or her care.

Is it reasonable to assume a patient suffering from severe asthma (370221004) would require a different care plan than a patient suffering from exercise-induced asthma (31387002)? How about the recently-discharged hospital patient who is suffering from asthma with irreversible airway obstruction (401000119107), referred to a primary care physician, whose Transition of Care problem list only states mild asthma (370218001)?

Sounds like a potential patient safety issue.

While the implications for care coordination are obvious and should be sobering, there are even more nefarious ramifications when you consider the EHR clinical data is also being shared with and consumed by insurance companies.

As more private payers are incorporating clinical document data into their member risk scoring and actuarial analytics, there are financial implications to the patient stemming from these automated (and, in some cases, arbitrary) mappings. The patient with mild depression (310495003) who is mistakenly coded as having major depressive disorder (370143000) may see medical insurance premiums increase, may experience a forced plan change with a reduced network of available doctors due to member attribution models applied based on clinical findings, and may have difficulty obtaining or renewing life insurance.

There are documented processes in place to rectify mistakes on consumer credit reports. This mandated SNOMED conversion introduces a new consumer protection need―how to address clinical data inaccuracies on a patient’s medical health record. A comprehensive process has yet to be introduced that would allow a patient to dispute a finding and have the information rectified at all points along the care continuum.

Is this what CMS had in mind when they mandated SNOMED for active and historical patient problems?

Welcome to the dark side of EHR interoperability.

April-17-2014

10:00

Brain Buzz 2014I admire those who can explain the complex simply. In researching the latest developments in neuroscience and technology, I discovered the brilliant Dr. Story Landis, a neurobiologist and the Director of the National Institute of Neurological Disorders and Stroke.

Dr. Landis is part of the leadership for the President’s new “BRAIN Initiative,” a Grand Challenge of the 21st Century, and provides an easy overview of the latest advances in neurotechnology in this video (starting at 5:05).

She presented at the Society of Neuroscience’s Annual Convention as part of a distinguished panel to discuss the new brain initiatives in the United States and in Europe for 2014.

What is the U.S. BRAIN Initiative?

The acronym, BRAIN, stands for Brain Research through Advancing Innovative Neurotechnologies.

According to the National Institutes of Health, “By accelerating the development and application of innovative technologies, researchers will be able to produce a revolutionary new dynamic picture of the brain that, for the first time, shows how individual cells and complex neural circuits interact in both time and space.”

The goal of the initiative is to develop tools for researchers to discover new ways to treat, cure, and even prevent brain disorders. Through these technologies, researchers will explore “how the brain enables the human body to record, process, utilize, store, and retrieve vast quantities of information, all at the speed of thought.”

Why Don’t We Have a Consistent Map of the Brain?

Neuroscientists need a consistent map of brain anatomy, but there isn’t one yet. Why? According to the Kavli Foundation, one of the partners of the initiative, “In the fast-moving field of neuroscience, researchers constantly reorganize brain maps to reflect new knowledge. They also face a vocabulary problem. Sometimes, different research groups will use several words to describe a single location; other times, a single word may mean different things to different researchers. Nor do maps remain consistent when moving across species.”

Advances in Neurotechnology to Visualize the Brain

The Connectome

A Connectome is a structural description of the brain first proposed by Olaf Sporns. The Human Connectome Project (HCP) is a consortium comprehensively mapping brain circuitry in 1,200 healthy adults using noninvasive neuroimaging, and making their datasets freely available to the scientific community. Get the HCP data here.

Four imaging modalities are used to acquire data with unprecedented resolution in space and time. Resting-state functional MRI (rfMRI) and diffusion imaging (dMRI) provide information about brain connectivity. Task-evoked fMRI reveals much about brain function. Structural MRI captures the shape of the highly convoluted cerebral cortex. Behavioral data provides the basis for relating brain circuits to individual differences in cognition, perception, and personality. In addition, 100 participants will be studied using magnetoencephalography and electroencephalography (MEG/EEG). – HumanConnectome.org

connectdome

Brainbow

Brainbow is the process by which individual neurons in the brain can be distinguished from neighboring neurons using fluorescent proteins. The idea is to color-code the individual wires and nodes, and was developed at the Center for Brain Science at Harvard.

Brainbow

CLARITY

CLARITY (Clear, Lipid-exchanged, Anatomically Rigid, Imaging/immunostaining compatible, Tissue hYdrogel) is a method of making brain tissue transparent, and offers a three-dimensional view of neural networks. It was developed by Karl Deisseroth and colleagues at the Stanford University School of Medicine.

The ability for CLARITY imaging to reveal specific structures in such unobstructed detail has led to promising avenues of future applications including local circuit wiring (especially as it relates to the Connectome Project). Pictured is a mouse brain with CLARITY.

CLARITY_brain

optogeneticsOptogenetics

Optogenetics uses light to control neurons that have been genetically sensitized to light. Optogenetics is credited with providing new insights for Parkinson’s Disease, autism, Schizophrenia, drug abuse, anxiety and depression.

A Revolution is Taking Place in Brain Science

Also part of the leadership for the BRAIN initiative is neuroscientist William Newsome of Stanford University:

Most of us who have been in this field in the last few decades understand that there is a revolution going on right now, so these tools we’ve mentioned already did not exist 8 years ago, and some did not exist 6 months ago. The pace of technological change is so rapid right now that those of us who were traditional experimental scientists say, “Whoa, what does it even mean to be an experimental scientist in this day and age?” We have to totally rethink what experiments are even possible, and it opens up vistas that were unimaginable 10 years ago.

Dr. Newsome recently wrote about the Initiative in JAMA Neurology:

“Missing, however, has been an understanding of how the many millions of neurons associated with a perception, thought, decision, or movement are dynamically linked within circuits and networks. Even the simplest perceptual task involves the activity of millions of neurons distributed across many brain regions. How simple percepts arise from patterned neural activity and how the resulting percepts are linked to emotion, motivation, and action are deeply mysterious. In the past, answers to these questions seemed out of reach.”

New Brain Health Registry

To get a deeper understanding of the brain before and after disorders, neuroscientists from the University of California San Francisco have established a new “Brain Health Registry.” Their goal is to address one of the biggest obstacles to cures for brain disorders – the costs and time involved in clinical trials. To register your brain, participate in games, and help scientists, read more in the FAQs.

The Brain and Disorders by the Numbers

The average adult brain is about 1,300 to 1,400 grams or 3 pounds, and is about 5.9 inches or 15 centimeters long. It is often quoted that are 100 billion neurons in the human brain, but Dr. Suzana Herculan-Houzel of Brazil recently discovered there are 14 billion fewer. According to her research, the human brain has 86 billion neurons or nerve cells.

What is the impact of brain disorders in the U.S.?

According to the World Health Organization, brain disorders are a leading contributor to the global disease burden, and the fourth highest for Western developed countries. About 50 million people in the U.S. suffer from damage to the nervous system, and there are more than 600 neurological diseases.

Psychiatric Illness – About 1 in 4 American adults suffer from a diagnosable mental disorder in any given year, according to the NIMH.

Alzheimer’s – In 2014, there are 5.2 million people in the U.S. with Alzheimer’s Disease, according to the Alzhemier’s Association. With the growth of the Baby Boomer generation, it is expected that between 11 and 16 million will be affected by 2050.

Parkinson’s – The Parkinson’s Foundation estimates 1 million Americans live with Parkinson’s Disease.

Autism – One in 68 children in the U.S. are affected by Autism Spectrum Disorder, a 30% increase from two years ago.

Innovation Requires a Multi-Disciplinary Approach to Research and Technology

The BRAIN Initiative involves a number of government agencies and private partners fostering a multi-disciplinary approach to research and technology. Specifically, it is a unique collaboration across disciplines involving the National Institutes of Health and the National Science Foundation. Learn more in this video with Dr. Tom Insel, Director of the NIMH, and Dr. Fleming Crim of the NSF, as they discuss exploring the connections between the life sciences and physical sciences in understanding the brain.

Call to Action from the White House

Through a Call to Action, the White House has asked to hear from companies, health systems, patient advocacy organizations, philanthropists, and developers about the unique activities and capabilities underway that could be leveraged to catalyze new breakthroughs in our understanding of the brain.

Do you have an idea? You have until May 1st to send your ideas to: brain@ostp.gov. 

April-8-2014

10:41

The month of April has brought with it another mass shooting at Fort Hood in Texas. This is the second mass shooting at the Army base since 2009 and according to Mother Jones, the 67th mass shooting in the U.S. since 1982.

Following mass shootings, two common public discussions seem to arise—gun control and mental health. Usually the two get blended together and it becomes about those with mental illness accessing guns. We’re not going to talk about that. In fact, we’re going to take guns out of the equation completely and strictly talk about mental health.

While mental illness is often brought up in the context of these shootings – it’s suspected shooters Jared Loughner, James Holmes and Adam Lanza may all have had some form of mental illness – mental illness does not necessarily drive those who deal with it to commit headline-grabbing crimes. Mental illness can be much more subtle and much more common than these incidents lead us to believe. In fact, the National Institute on Mental Health estimates about one in four adults ages 18 and over suffer from a diagnosable mental disorder in a given year.

Mental health disorders include everything from depression, bipolar disorder, suicide, schizophrenia, anxiety, obsessive-compulsive disorder, PTSD, eating disorders to personality disorders. Yet, despite the common nature of mental illness, those experiencing mental health issues may have difficulty getting treatment.

Mental illness still carries a huge stigma, causing embarrassment for those with mental illness that often prevents them for reaching out for help when they are struggling. Fear of being judged as unstable, potentially violent or “crazy” can prevent those with mental illness from getting the help they need. Access to mental health care also has historically been a challenge.

Because coverage for mental health issues was not on equal standing with coverage for other medical issues, Congress passed the Mental Health Parity Act in 1996. This law prohibited large employer-sponsored group health plans from imposing higher annual or lifetime dollar limits on mental health benefits than those applicable to medical or surgical benefits. In 2008 Congress passed the The Mental Health Parity and Addiction Equity Act of 2008 to close some of the holes that where in the original parity act.

Still, just as with other medical issues, factors like living in a rural area, income, insurance, and other factors can hinder access to care.

Technology is one possible way to help breakdown stigma and barriers to care and can provide a tool to help raise awareness and build support. Here are some ways the tech community can help those with mental illness.

Therappy is an online community of discussion forums. Therappy’s goal is to become the leading community and source of information for everyone involved in mental health technology. Therappy is a new community, it just launched in March, and it is looking for those with an interest in how technology can affect mental health to participate in the discussions.

Target Zero to Thrive is the Depression and Bipolar Support Alliance’s new social media campaign that runs during April. The campaign “challenges mental health care professionals, researchers, and individuals living with or affected by mood disorders to raise treatment goals to complete remission—to zero symptoms.” As the organization points out, cancer treatment sets a goal for a patient to become cancer free and the same standards should be for treatment for those with mood disorders. Reducing symptoms is not enough, the target of zero symptoms is what’s needed for patients to thrive. By visiting the Target Zero campaign site, you can find ways to support the campaign this month.

WeCounsel is an online counseling website for mental health care providers, patients and healthcare organizations. It provides users with a HIPAA-compliant telehealth platform that connects mental healthcare providers to their clients online. The website allows secure and confidential videoconferencing for online counseling sessions.

Getting the word out about mental health resources was the focus of a recent challenge sponsored by Johnson & Johnson. Participants were tasked with coming up with ideas to increase awareness and use of mental health services for depression and anxiety disorders. The winners of the challenge where Tulane University graduate student Alejandra Leyton and University of Maryland medical student Veena Katikineni. The pair proposed a solution called MHealth for Mental Health which  is a free SMS text service that sends “relevant information to people between the ages of 15-49 years old who present with symptoms of depression or anxiety, as well as the community at large with the hope that members will refer one another to the service.”

Smartphone apps are another possible tool for improving mental healthcare. It should be noted that while many exist, few have hard scientific evidence to back up their claims of effectiveness, say researchers at the Black Dog Institute at the University New South Wales in Sydney, Australia.

But some users already swear by them. A recent New York Times article, delved into the use of mindfulness smartphone apps to combat anxiety. The ease and convenience of use is a main part of the appeal of these apps.

If you want to join in the mental health discussion on Twitter you can follow the hashtags #mentalhealth #mhtech #mhchat or #mhsm.

April-3-2014

9:15

2013 significantly changed the context of the healthcare security and privacy conversation. From the Snowden NSA revelations, to HIPAA Omnibus rule, changes in breach characteristics, to connected devices, mhealth, IoT and increasing use of cloud and corporate BYOD policies, one thing is clear: security by obscurity equals no security at all. The burden of protecting PHI is now spread across all data holders, patients, providers and payers alike. Outlined below are some of the unique security issues that will need addressing as healthcare technology moves into a data analytics mindset.

Breach Characteristics

More than 7 million patient records were exposed in 2013 alone, marking a perceived 138% increase from reported 2012 healthcare data breaches. Expect to see a change in how breaches occur, and keep in mind, an uptick in breach notifications doesn’t necessarily imply an uptake in actual data breaches. Everyday PHI breaches of years past went largely unnoticed whereas now technology helps track and log access. 2014 will see a new focus on targeted identity theft and less focus on lost laptops and stolen hard drives. Human error still accounts for 75 percent of all healthcare data breaches, but medical-related identity theft accounted for 43 percent of all identity thefts reported in the United States in 2013.

Federal regulators are planning for a more permanent HIPAA audit program to support the 2013 HIPAA Omnibus rule, and industry can expect increased scrutiny for violations pertaining to inappropriate disclosure of data and denial of patient access. What has not yet been directly addressed is if the NSA has accessed, reconstructed or inferred any personally-identifiable information covered by HIPAA, such as that through Google, Microsoft, Apple, and through mobile games, and how a BAA will hold up in such a data collection scenario. Currently, cases are being heard regarding the warrantless access of state controlled health databases by other federal agencies, and the verdict has been in favor of patient privacy.

Patient Best Practices Awareness

In other sectors, user data purging, and security tools are entering the mainstream. Apps to help consumers navigate terms of services and platform data deletion shortcuts to password managers, and tools to avoid search and web tracking are helping users gain control of their personal information. But when it comes to healthcare, how common is it to leave a credit card on file or how often do patients really check their charts for errors?

The internet of things, and connected reality as it plays into mobile and personal health apps adds another layer to patient security awareness. Malware attacks through network connected appliances such as refrigerators, HVAC and media centers have been of concern recently, and they present an unsuspecting entry into a home network. What used to be as simple as using a WPA key on a home router and not handing out a SSN is now a different conversation. Enterprise security has long favored an onion type approach, or defense-in-depth, but that’s far from the case with personal information security. And the question remains, is defense-in-depth even effective in the personal security space, given its shortcomings in enterprise IT?

PHI in the Cloud

Healthcare IT is finally trusting cloud storage and computing. As of 2013, 30% of healthcare organizations are leveraging cloud technology, and nearly twice that are confident in the future of cloud security. Other industries have proven that cloud computing can be a safe, economical, collaborative and scalable approach to enterprise data management problems. While cloud security will garner much of the spotlight for the next several years, the privacy aspect of distributed data liquidity must be addressed.

Currently, there are no HIPAA restrictions on the use or disclosure of de-identified health data, even though 87% of all Americans can be uniquely identified using only zip code, birthdate, and sex. PHI is currently, and will be increasingly, sold to third-party data warehousers, insurers, pharma, marketers, researchers, and more. Current standards for anonymized data do not prevent positive backwards identification. This is the conversation the healthcare industry, and patients, should be having in 2014 regarding cloud computing.

Corporate BYOD

Sorry, but that cat left the bag 5 years ago. Employees are using their personal devices at work, regardless of policy. The best bet to mitigate BYOD security risks is to address it head on, and support secure solutions that enable user’s workflows. Secure SMS and texting has been solved. HIPAA compliant platform-as-a-service is a thing. There are mobile apps to address medical imaging, rounding, clinical diagnosis, EHR integration, and countless vendors are developing platform-down solutions for providers.

Beyond mobile security, and BYOD policy, the issue will be how breaches on these devices will be reported, and analyzed. Currently, the HIPAA Wall of Shame classifies all mobile device breaches under the catch-all “Other Portable Electronic Device” which as mhealth really enters the mainstream, will be a near useless designation.

Mobile Health Security

In this context, mHealth refers to medical apps used by patients, not wellness/fitness apps nor clinical practice or reference apps. Current efforts in the private sector to certify mobile health applications have failed, largely due to a lack of understanding around mobile health security. Mobile apps and devices come with complex challenges not seen elsewhere in healthcare, particularly around workflow data integration, security and user experience. Two camps have emerged: platform-down apps such as those from athenahealth and Greenway, and independent shops like AliveCor and Glooko who have yet to meaningfully integrate into major vendors. The third obvious play would come from valley tech giants, but despite rumors, nothing of substance has been shipped.

While certain security best practices should never be skipped (encryption, SSL, passkeys, etc), user experience should come first and foremost. Security is nearly insignificant if no one uses an app, and patients will not tolerate poor design. Many questions remain regarding shortcomings of FDA mhealth software regulation. Are medical providers the best individuals to evaluate a mhealth app for security and patient usability, and how may the design, developer and infosec communities better help educate the medical community? It will be important to address provider shortcomings in prescribing and recommending patient-facing mhealth tools, especially around efficacy, privacy and security.

Here are the chat topics I proposed during the #HITsm chat, April 4. I would love to hear your feedback on the topics, or any other related issues, below in the comments.

  1. Does theft of your electronic health record cause more concern than theft of other private info?
  2. Should there be different security requirements for govt access to PHI data vs others?
  3. How can the health IT infosec community help journalists/consumers/patients evaluate mobile apps and enterprise health IT solutions?
  4. Are docs qualified to RX and recommend health apps? How can mHealth be transparent regarding PHI risks?
  5. Should patients be allowed to opt out of the sharing of their anonymized PHI data if used for profit? If so, how?

April-2-2014

9:03

While no formal announcement, Mac Rumors swirled last week about HealthBook and the iWatch.

Screen images of health book leaked onto 9to5Mac, showing what an Apple Health Book, presumably integrated into iOS, might look like. Reports highlighted several health measurement areas, some with obvious hardware and sensor connections, others without.

apple

Some of the most interesting areas of focus include blood pressure, hydration, respiratory rate, nutrition, blood pressure, oxygen saturation and blood glucose, and even “blood work.”

Could these be sensor-driven? For most there are no current, effective ways of effectively sensing these measures outside of the clinic, and the primary reason this could be truly disruptive. We’re left to speculate at this point, but if these are sensor-driven, we’ll have something that will be disruptive not just in terms of quantified self and technology, but in medicine.

Apple knows that medicine, since medicine has been a science, is about quantifying us. Quantified self is not some passing fad, it’s the future of medicine. What’s changing–what’s revolutionary–is who will be doing the quantifying and where; pushing medicine, as Christensen predicted, to low-cost care centers.

Apple is indicating that quantified self’s future is not selfies for health nuts. Soon, it will be medicine.

I’m reminded, and have high hopes, that this is reminiscent of when Apple created the PC market at a time when people thought that only computer scientists would use computers. The idea now, like then, is to empower people with what technology can do.

My own speculation and hope is that this will be a major first step toward an integrated environment or platform where many of the things that used to be measured in the clinic can be measured seamlessly at home, to make transitions and communications simpler.

A New Platform for Healthcare?

Health tech strategist Vince Kuraitis recently tweeted: “For wearables, Google likely views Android SW as “platform”;  Apple views combo hardware (phone/watch) + iOS SW as platform.”

I tend to agree, and a larger question may be whether the sensors for HealthBook would be a part of the platform, or would Apple just do some easy integration? My sense, and my hope, is that sensors will be separate from “platform,” and would connect like apps use the current iPhone. In that way, Apple could grow sensor ecosystems that would be open and available to further innovation. Given Apple’s history of tight control over hardware, it’s very much an open question whether they will go in that direction.

Could Apple operate and sell a network of sensors more like they operate the app store? What kinds of criteria might they need to meet to ensure quality? Will they provide a platform to connect to the iOS? If so, which iOS platform (iWatch, iPhone or other)?

With software and apps, of course, Apple has maintained a walled-garden approach. Much of the innovation takes place in the app world while preserving some control over what apps are available in the app store. Innovation occurs with the hardware and the user experience. I hope the trend for a walled-garden platform continues making available a network of sensors and sensor apps, much like the app store does for software.

According to venture capitalist MG Siegler (via Wired), “Healthbook could encourage Apple to build more bridges between its devices and third-party sensors, making it easier to find, say, a high-end heart-rate monitor that works with your iPhone.”

Wired goes on to say, “Apple Healthbook won’t just promote the fitness of iPhone users. It will boost the well being of an entire ecosystem of healthcare technology companies.” calling Apple’s play “something huge.”

It’s also easy to speculate that Apple could go the way of Google Health and Microsoft HealthVault. What can Apple do differently to avoid the less-than-disruptive recent history of PHRs?

Make It Seamless

The first, is to make it low-touch, to automate everything. Apple notoriously likes to keep a clean, uncluttered hardware environment to control the user experience, but has also consistently allowed for peripheral devices to interact to say, play music from you iPhone, or get input from your Fitbit.

It’s hard to imagine that Apple, as user experience experts, would expect users to input information manually, so there’s plenty of speculation about what sensors might be on the way.

We’ve come to expect success from Apple, largely due to their focus on user experience. I can’t help but dream of a watch that could measure all of these things, but it just doesn’t seem doable in the near future. It’s also hard to imagine some kind of network of sensors.

Will there be a community, or a social component?

Make it Social

Most successful apps act as communications tools of one kind or another. Will HealthBook offer the same? Or, will they use third-party sharing and communities like Facebook?

Make it Ours

As I mentioned in iHealthBeat on the Social Economy of Healthcare, I hope Apple will find a way to treat the community ownership-aspect appropriately, as their recent history would suggest. Personal assets such as users’ privacy must be deeply respected while nurturing personal, transformational communication.

Make it Simple

My final wish for the HealthBook is that they make it something my mother can use. Consistency is key, particularly for the elderly. An elderly person or caregiver may have an iPhone, but learning new ways to use it can be intimidating. Make it be simple and seamless, like so many of Apple’s products, enabling the consumer to take care of our own health in a meaningful way.

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