Five Essentials of a Population Management Digital Health Technology

Population health management (PHM) means different things to different people. It is a term which is in vogue now because of its injection into healthcare provider payment models and describes a strategy in which individual and patient subgroups are cared for outside of the traditional office and hospital sites.  In a previous post I discussed why population health management matters.  Such a strategy necessitates technology which interacts with patients and their caregivers in a bidirectional way with clinicians and ancillary staff.  These interactions should involve both objective data and subjective insights from patients.  In this piece I’d like to give my thoughts on what constitutes ideal patient management technology irrespective of one’s definition or the long-term survival of ‘population health management’ in payment models.

  1. Patient-facing portal. I was taken aback in discussions with some PHM technology vendors whose product had no patient-facing aspect.  Such an approach defeats the mission of PHM which by definition incorporates patient self-management and behavior modification. The aim of PHM is to extend care outside of the brick and mortar of traditional venues, transforming points of living into points of care. If most chronic diseases are preventable in large part with lifestyle changes, then PHM must extend to the patient/consumer and the places where these choices are made.
  2. Mobile platform. Any technology which addresses direct patient care today needs to be on a mobile platform.  It is unfortunate that mobile versions of most electronic health records are woefully inadequate today. The mobility of a PHM platform should be a given, since it is something both providers and patients expect today. Due to the critical nature of patient/consumer involvement required for self-management, mobile is a prerequisite component for success because most patients and caregivers expect this from any digital technology.
  3. Connected care tools. PHM is all about collecting data and at the same time transforming it into relevant  Objective information via patient-generated data sets is a critical component of PHM.  The types of data sets collected would hopefully depend upon the patient or disease state population in question.  The data must be filtered to be relevant to both clinicians and patients.  Patient-derived data tools (relevant to vital signs usable for heart or lung disease, diabetes, activity tracking, medication adherence, behavioral health) may be provided as third party technologies or proprietary to the PHM platform vendor. Patient-derived data also comes in the form of subjective data. Objective data must be supported with patient reported outcomes which provide necessary real world evidence. Together, the objective and subjective data complete a picture that benefits the individual patient (who can ideally view personal data relative to others in the population examined) a targeted patient population, the clinician, payer, and other stakeholders in HIPAA compliant ways.
  4. Real-time analytics. There is no arguing that Big Data is critical in healthcare today.  What is underappreciated is the value of real-time analytics to enterprise operations, patient safety, and better clinical outcomes.  Yet analytics are woefully underutilized by healthcare enterprises.  In a Deloitte Center for Health Solutions 2015 US Hospital and Health System Analytics Survey of CIOs, CMIOs, and senior IT leaders, less than 50% had a clear integrated analytics strategy.  As correctly pointed out in the survey, value-based care will necessitate such a strategy in order  “…to blend financial, operational, clinical, and other data to achieve their goals of improving quality, providing access, controlling cost, and managing provider networks…”  A platform which curates such data in real-time with customizable analytics by each stakeholder (including patients themselves) is what makes the data relevant.  Big Data collected by payers (including Medicare), regulatory agencies and others is often up to 12 months old. Process and patient outcome improvement as well as patient satisfaction cannot be dependent upon such outdated information.  In addition, the data is raw, unfiltered, and not user-specific. Enterprises need to pivot quickly to provide the best services in the most economical way.  The ability to have current accessible relevant data is expected and available in all business settings.  It is imperative and even more important in a sector of the economy which utilizes almost 18% of the USA’s GDP.
  5. Social interaction. As stated above, active patient participation in a PHM is critical.  This relates primarily to the importance of human behavior as a driver of health, wellness, shared decision making and treatment.  The importance of social media in healthcare cannot be underestimated.  Social media in healthcare involves both patients and clinicians exemplified by such online sites as  Mayo Clinic Direct.  Such interaction promotes communication which improves treatment adherence as well as serving as a source of patient reported outcomes (PRO) data (see above).  The value of online patient communities has been appreciated for a while now and supports a comprehensive PHM strategy.

As one can see, PHM is a concept which is not standardized and developing both in terms of strategy as well as the role of digital technology in it.  I am eager to observe how the challenges of digital divides, patient behavior, and the culture of traditional healthcare affect the extent and rate of adoption of PHM strategies. I look forward to and am optimistic about the future in this regard.

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Five Reasons Why Digital Health Needs Patient Reported Outcomes

According to the National Quality Forum, patient reported outcomes or patient reported outcomes measures (PROs) can be defined as “any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else.”  Initially utilized as a clinical research tool, they have since become a critical component of assessing quality of care.  Digital health technology has long been touted as a way to promote patient engagement. There is significant opportunity for patient and other stakeholder benefit in the incorporation of PROs into any patient-facing digital health technology. In this piece I will try and give a focused look at PROs, why they are becoming important and the interplay with digital health technologies.

1. PROs are now determinants of healthcare payments. A good example of the interplay between PROs and value based payments can be found in a discussion of the Function and Outcomes Research for Comparative Effectiveness in Total Joint Replacement (FORCE-TJR).  Some of the lessons learned and described from over 25,000 patients reporting into the registry are interesting and worth sharing because they are universal and not specific to joint replacement; (A) PROs provide actionable data to help guide clinical decision-making. (B) Identifying key risk factors…can go a long way toward managing the entire 90-day episode of care and successfully participating in value-based contract gainsharing.  (C) Making PROM data collection easy for both patient and surgeon’s office is key to success. (D) Post-TJR PROMs can be a valuable patient satisfaction and engagement tool. PROs are not just represented by patient satisfaction surveys.  Payment for care is also tied to PROs relating to patient engagement and clinical outcomes.

2. PROs put the focus back on the patient. Quality of care can only be determined by the patient.  Treatment goals can vary substantially for individual patients with the same clinical problem, whether it is acute or chronic. In this way, care can be liberated from a ‘one size fits all must meet strict evidence-based guideline model’ which dominates pay for performance payment systems today.  PROs can help evaluate the same patients in a longitudinal fashion (at various points in time), maintaining the subject, not necessarily the disease or treatment as the focal point. In this way, patients themselves, armed with PRO data can change the direction of treatment with the identification of logistical, medical, emotional or financial challenges which might adversely or positively affect care.

3. PROs can be used to close gaps in care. Disparities in healthcare in the USA have been well documented. PROs can be used to study patient populations with similar clinical problems from diverse ethnic, socioeconomic, and geographical demographics.  By comparing standardized PROs, best practices might be readily identified.   One recent interesting study examined the disparity between patient reported outcomes measures and assigned triage priority levels for elective surgical procedures in British Columbia, Canada. This is an excellent example of how patient generated data can crystallize an inherent pitfall of healthcare policy and transform it to benefit patients.

4. PROs add a necessary dimension to clinical trials  The incorporation of PROs in oncology care has proven feasible and demonstrated to result in better patient outcomes. PROs are used in various ways in clinical trials.  Some of these include measuring study endpoints, monitoring adverse events, quality of care, symptoms, and longitudinal reporting during the study. PROs provide critical information to longitudinal follow-up of patients either in clinical drug, device or intervention studies or specific population registries.  A fertile area for the use of PROs is comparative effectiveness studies. Pharma needs real world evidence for post-market surveillance, reimbursement support, and relationships with patients. The use of PROs in clinical trials supports those goals. PROs via mobile technology in clinical trials facilitate patient engagement as well.

5. PROs provide real-world evidence. The FDA’s recent Draft Guidance on the Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices is a progressive acknowledgement of the value of non-clinical trial data in evaluating medical devices. Real-world data is data collected from sources outside of traditional clinical trials. These sources may include large simple trials, or pragmatic clinical trials, prospective observational or registry studies, retrospective database studies, case reports, administrative and healthcare claims, electronic health records, data obtained as part of a public health investigation or routine public health surveillance, and registries (e.g., device, procedural, or disease registries)…” Digital health is critical in this type of process, because “…The data is typically derived from electronic systems used in health care delivery, data contained within medical devices, and/or in tracking patient experience during care, including in home-use settings.”  Part of this data will certainly include PRO data which will aid in the FDA process.

As described above, PROs are becoming an integral part of many facets of healthcare: quality assessment, payment models, clinical trials, product evaluation, and patient engagement with digital technologies.  The culture of healthcare is slowly (isn’t that always the case?) shifting to an appreciation of patient reported outcomes and patient generated data. Let’s look forward to PROs becoming something which patients expect to participate in and to benefit from …for that is what will ultimately change healthcare.

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Five Reasons We Must Untrap Digital Health’s Big Data

We tend to think of Big Data as a Land of Oz where we can find any information we need. What is not appreciated by most in healthcare is that there is a lot of Big Data collected but most is not relevant data, nor is it easily located or even accessible. The implications of this are enormous. The true value proposition of Big Data in healthcare lies in how the data is analyzed and presented.  In my last post I discussed ways in which Big Data can be transformed into relevant data.  I will discuss five reasons why freeing up ‘trapped’ data is critical to making it relevant.

  1. Population health management. Analytics are the building blocks to population health management, one of the new drivers of healthcare payment models. It is interesting to note that a 2015 HIMSS Analytics Survey found that 67% of surveyed organizations had population health management programs in place but only 25% of them utilized an outside vendor solution to address their needs. Population health management’s purpose is to emphasize shared decision-making and care between the clinician and the patient with an emphasis on accomplishing this outside of the healthcare facility or clinic. While this has always been the aim, we now have digital tools which can facilitate this as well as the shortage of providers increasing the demand for such tools.
  2. Patient outcomes data. There is a major discrepancy between quality measures and patient outcomes. According to the National Quality Data Clearinghouse only 139 of its 1958 quality indicators are patient outcomes and 32 are patient-reported outcomes. As correctly pointed out in a recent piece in the New England Journal of Medicine, we have used evidence based medicine metrics as a substitute for patient outcomes.  The International Consortium for Health Outcomes Measurement (ICHOM) has already approved 20 sets of outcomes standards and projects to have over 50% of disease burden covered by 2017.  Patient outcomes data whether derived from data sets directly from the electronic health record (EHR) or from patient-derived data emanating from mobile or other digital technology is critical to determining outcomes. The problem lies in how to collect data that lie with disparate EHR or other digital formats.
  3. Support digital and mobile clinical trials. Imagine discrete data                                 from any individual clinical trial patient’s point of care (regardless of the provider’s EHR brand) coupled with direct patient-generated data delivered to a mobile clinical trial platform. Such a scenario is possible and will save Pharma and CROs huge amounts of money, improve accuracy and decrease lost or missing data. In addition, integration and interoperability between hospital records and data registries that flag patients seen urgently at a distant site helps save lives.  In this way a patient in a clinical study, even winding up in an out of town emergency room can ideally have pertinent data automatically sent to the clinical trial platform.
  4. Save money. Harnessing relevant data from Big Data can save money in many ways. As mentioned above, the costs of clinical trials can be markedly decreased. Organizations (governmental, healthcare enterprises, professional societies) who maintain clinical registries by definition require relevant longitudinal data. The ability to seamlessly collect relevant data automatically across disparate EHRs results in a more complete picture of the cancer or chronic disease patient with comorbidities. Doing so can hopefully improve outcomes translating in higher value based payment. In another example, it is not uncommon for a cardiac patient to be in separate mandated registries for coronary stents, a heart valve, and/or an implantable defibrillator.  A single database collecting all the longitudinal data on such a patient would save millions of dollars to the sponsor and the provider (via improving efficiencies of clinical data staff) in addition to contributing to outcomes measures in a more meaningful way.
  5. Other stakeholders need real-world evidence. Just as a picture is worth a thousand words, relevant collected data helps pain the real picture of the patient. Post marketing surveillance registries and studies of drugs and medical devices by industry need relevant discrete data sets provided in an efficient way. Payers need this data to determine true outcomes. Providers need relevant data to determine true outcome measures, quality metrics, and inputs for value based payment models.

One can see how static Big Data is useless without the ability to make it relevant by ‘untrapping’ it. The technology exists. It can save time, money, and address interoperability challenges.  The critical importance of patient outcomes becoming the focus of quality measures for use in new payment models, drug and device development, and clinical trials will drive adoption of such tools in the near future.


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Five Ways to Turn Big Data into Relevant Data

‘Big Data’ may be defined as data that is so large or complex that it cannot be processed by the usual applications. Current common uses of Big Data in healthcare include the collection of genomic data, insurance claims data, medication prescription data, mandated hospital reporting data (admissions, diagnoses, readmissions, other).  The problem with amassing such data is that much of it is trapped in silos across the healthcare continuum and not available (even for studies obtaining consent and protect privacy) for analysis.  Robust analytical tools are not a part of most IT platforms utilized at points of care.  Organizations which can profoundly influence treatment guidelines incorporated into evidence-based medicine, healthcare policy and patient advocacy are not availing themselves of relevant data. I will discuss some ways in which Big Data can be transformed into little or relevant data.

  1. Longitudinal follow-up. As a clinician I appreciate the enduring contributions of  Sir William Osler, considered to be the father of modern medicine.  One fundamental concept he championed was the study of the natural history of a disease and the longitudinal follow-up of treated patients. The recording and storage of data longitudinally is called a registry.   Registries can serve as potential sources of relevant data. If data set query is large enough, the right questions are asked of the mined data and adequate and creative analytics are applied, it’s not difficult to transform Big Data from registries into relevant data.
  2. Obtaining data across disparate digital platforms. Following a patient longitudinally often necessitates obtaining data points acquired from providers who utilize different EHR platforms. This presents problems for clinicians, epidemiologists, and patients and their caregivers. This cross-logistical data is critical for registries. A patient participating in a clinical trial or registry unexpectedly seen in an emergency setting away from home or even by a specialist nearby with an EHR different than that of the patient’s registry clinician/investigator presents both logistical and technical challenges of incorporation of pertinent data into the patient’s registry profile. A platform which can ‘scrape’ data of a specific patient across EHR silos assures the collection of all relevant data.
  3. Customizable Interfaces and analytics. Not all clinicians desire to view all or the same data collected on patients involved in a registry or population health IT tool.  They might want to view the same data in a different visual context. This type of customization is a welcomed reprieve from the confines of traditional EHRs and registry tools. Customization of this type transforms Big Data into relevant data for clinicians. Looking at data in different ways can stimulate the viewer to approach a patient, group of patients or a treatment plan in a different manner.
  4. Applying best practice guidelines and evidence-based medicine results. A data collection platform which furnishes evidence-based practice guidelines for treatment of a specific disease and provides the ability to compare real-time individual or population health group data to outcomes of optimally treated patients can be very powerful.  It thus provides a direct connection of big data to relevant data, allowing for mid-treatment corrections during care to meet best practice outcome metrics. In addition it can serve as a mechanism to correct the significant geographic variations in healthcare.
  5. Incorporation of patient-reported outcomes measures. Patient reported outcomes measures (PROMS) have been described with relevance to healthcare economics for over eight years. PROM is an integral part of the value-based payment system which will dominate how healthcare will be paid for.  These outcomes are determined from the collection of specific clinical data sets. Population health management IT tools must incorporate these measures to meet payment requirements. This goal can be accomplished in a piecemeal fashion with multiple IT vendors or ideally with one.

Much has been said about the power and prospects of Big Data. But this data, like the food we eat needs to be distributed, filtered, and relevant parts applied to good use. Healthcare may lag behind in the use of digital and mobile technologies, but the amount of Big Data garnered by all its stakeholders is incredible. The future of improvements in healthcare (whether they be in cost savings, clinical outcomes, efficiency or other areas) lies in the way in which Big Data can be made relevant. It’s like the old adage, “All politics is local.”

As a disclosure I am an advisor to Pulse Infoframe which has capabilities to accomplish all of the above.


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My Five Asks of Digital Health

Digital health is experiencing what I would characterize as its adolescence.  The rudimentary pieces are in place for adoption; Awareness of the technologies, the progressing maturation of mobile technologies, realization of its critical need in the marketplace, interest by large companies (though with widely variable levels of commitment and material projects), and development of breakthrough technologies. Changes occur slowly in healthcare but they need to accelerate because of the increased urgency.  There was once a time when patients would refuse to see a physician assistant or nurse practitioner. These providers are now integral parts of the healthcare team and patients value them. I will now touch on missing puzzle pieces which, if addressed, can substantially impact the mission of digital health.

  1. Comprehensive and standardized telehealth laws. According to The National Business Group on Health’s 2016 Health Plan Design Survey (free with sign up)  employees can expect “…More resources and tools to help…navigate the health care system: Care shopping tools, care decision support resources, and telehealth.” This increased acceptance and expansion of telehealth services must be preceded by regulatory and legislative changes addressing payment and professional licensing issues. Telehealth itself speaks to society’s mobility, direct and indirect costs of in-person care, and healthcare professional shortages. The time has come for telehealth to become the norm and in-person visits to supplement this under-appreciated and underutilized modality of interaction.  The immediate expansion of telehealth into mainstream care by all payers, public and private is necessary.  Healthcare professional licensing reform is also necessary to decease the red tape and expenses of telehealth. Patient safety will benefit with the increased transparency of professional  conduct bought about with a Federal license.
  2. Clinically designed and connected electronic health records (EHRs). The Federal incentive program called Meaningful Use  has essentially achieved its goal of widespread adoption of EHRs. What has yet to occur is the presentation and flow of data by the large vendors in a way which is intuitive to users.  EHRs were designed to meet data requirements of regulatory and payment agencies. Clinicians have become data entry technicians and spend less time interacting with patients.  According to a recent study an estimated 785 hours/year is spent per physician on data entry to satisfy payment requirements for the documentation of quality measures.  The two biggest problems with EHRs today are usability (presentation of the interface and clinically oriented workflow) and interoperability.  Clinicians or testing centers utilizing disparate EHR systems are unable to share data.  This includes the ever-expanding sector of retail healthcare centers (pharmacy or urgent care centers) as well as home care organizations.
  3. Wearables as remote monitoring. The utilization of remote patient monitoring  (RPM) is increasing.  RPM has entered the spotlight as a means of decreasing hospital readmissions which now result in Medicare payment penalties. However, the benefits in this regard to have not been demonstrated on a large-scale and the success might very well be tied to other factors mentioned in this piece. In addition, the reduction of readmission rates has not translated to improved patient outcomes. The proliferation of wearable sensor technology in the consumer realm has accelerated exploration in the traditional healthcare market for this technology, yet there are substantial differences between these markets.  Bolstering interest in wearables by strange bedfellows as sports equipment companies and medical device manufacturers is the desire of the healthy aging population of baby boomers for unobtrusive monitoring technologies.  Wearables can easily fill that order but according to a  survey on wearables by AARP as part of a six-week trial, “…participants also said the devices’ design and utility are lacking in features that would encourage long-term use or adoption. The gap between expectations and reality indicates a significant opportunity to better serve the 50-plus market, the study concluded.”
  4. Better payer-enterprise partnerships driving needed sharing of analytics and data. As the healthcare payment model in the USA shifts from fee for service to value- based (which considers quality performance measures, outcomes, and patient satisfaction), the importance of data analytics becomes clear. We will see a shift of responsibility for the collection and analysis of patient and care management data from the payer to the provider. Analytics will be the best way a provider can track performance quality, efficiency, and interventions affecting patient outcome. This de-identified data will benefit both payers and providers and might ultimately become a commodity sold to multiple payers by providers. This scenario dovetails with the massive consolidation we are seeing in healthcare. It remains to be seen how this all benefits the patient/subscriber. However, the hope is that the more available and granular the data, the more transparent the costs of care vs outcomes might become.
  5. Incorporation of social media in healthcare. Social media is the most underutilized resource available to all stakeholders in healthcare. While there are understandable concerns and barriers to unbridled participation in social media by healthcare enterprises, payers, Pharma and other stakeholders, there remain huge opportunities to help patients and caregivers via social media which can direct them to other sources of disease-specific educational content. The current focus on population health management as public policy as well as basis for payment could greatly benefit from data derived from social media discussions on healthcare. How that is designed and processed is a potentially powerful collaborative project among many stakeholders including patients.

Plans for improvement of the current healthcare system must consider technology a critical component.  Public healthcare initiatives and market stresses require it. All of the asks above are doable now. It is up to patient advocates to demand them and decision makers to implement them.

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Five Digital Health Implications of the Most Important Report You Never Heard of

One of the most discussed barriers to the adoption of mobile health technologies is the Digital Divide between the elderly and the rest of society. Technology may support initiatives encouraging aging at home.  There are many reasons why older persons do not use technology.  A recent report by the President’s Council of Advisors on Science and Technology entitled “Independence, Technology, and Connection in Older Age” highlights many of these issues.  I praise the report because it considers caregivers, socioeconomic, geographical and clinical aspects of older people both healthy and sick.  It also provides clear recommendations to the President in the form of policy initiatives and actionable items (some of which require legislation which unfortunately might delay or prevent implementation).  One can only hope that they can be enacted because of both their urgency and potential significant benefits.  The report is divided into three sections and explores how technology can impact them: social engagement and connectivity, cognitive function, and physical ability. There were several common themes in the major areas of the study:  heterogeneity of the older-adult population; the foundational role of Internet connectivity; monitoring within homes and communities; the need for more research; and technology standards. I will now discuss what I believe are the five most significant takeaways from the report.

  1. Socialization via technology. In its report, the Council clearly highlights the fundamental importance of social interactions in human well-being. Besides the most popular sources (Facebook, Twitter, Pinterest, Instagram), virtual communities (which might include medical peer support group sites), and  real-time video conferencing, in-person facilitating websites (companionship/dating sites, Meetup).  Examples of technologies which facilitate care.  Honor is a technology in which older adults can participate in their own care in a convenient way. It includes a ratings feature which can help improve the experience over time via customization.  Social media can also facilitate volunteer and employment opportunities.
  2. Technologies can address cognitive decline. The report addresses three opportunities in the area of cognitive function. It discusses how in-home and wearable sensor technology can identify changes in behavioral patterns of medication use and daily activities which may signal changes in cognitive function. Technology can assist in the prevention of economic fraud and exploitation, unfortunately commonly experienced by the elderly. The third opportunity lies in the potential for technologies to maintain or enhance cognitive health. Technologies available today can help monitor those with established cognitive impairment. An excellent example is a shoe inner sole geolocation which can track  a potential wanderer. Simple digital music technology has been demonstrated to improve cognition. Not to be underestimated is the need for  large scale educational efforts to increase awareness, healthcare and social community support, and adequate safeguards in the area of privacy and security of technologies.
  3. Technologies to address physical ability. The report appropriately cites the various definitions of mobility from a healthcare standpoint.  It reviews the value proposition of telemedicine for those with physical problems limiting healthcare visits. This would result in increased access to care both in primary and specialty care.  The encounters can be either synchronous or asynchronous.  Much work remains to be done in increasing expanding payment for telemedicine.  The authors accurately discuss the need for increasing broadband access for technologies like telemedicine. Other challenges include professional cross-state licensing, and both human and process implementation issues (none of which are insurmountable and supported by existing examples).  Recommendations to improve functionality [(to have HHS work with the Department of Housing and Urban Development to improve functionality of home designs), product designs (medication and food packaging), wheelchair functionality] highlight social and other aspects of life necessary to optimize and accommodate physical ability.
  4. The report’s thread of aging at home. Older people have overwhelmingly stated that they want to age at home and not in institutions. The report’s theme supports a multidisciplinary approach which includes technology, public policy changes, support for caregivers, and consideration of environmental/social factors. AARP has established a $40M venture fund for technologies aimed at aging at home for its members. Aging at home must be a consideration in development of digital health technologies for older adults.  Unobtrusive, secure, and wellness-focused tools will have the best chance of success.
  5. Significance of the report for digital health itself. The Council should be praised for giving weight to increasing awareness and education about digital technology in general. Complimenting this is its focus on increasing access to technology and physicians via expanding broadband and use of telehealth respectively. Decreasing the Digital Divide by getting older people utilizing the Internet and mobile devices is a challenge of widely varying geographical significance.  Policy change recommendations by the Council in achieving this goal are to be supported by organizations like HIMSS, the AMA, and patient advocacy groups.

This report has implications for the digital health technology industry, for government agencies (who should look at aging at home as a national priority on many levels), and for the entire population who deserve the opportunity of doing all they can to age at home. Dignity is a universal value desired by the elderly. Technology has the ability to help people achieve this even in illness. I would like to thank the members of the Council for the contents of this report and look forward to seeing many of its recommendations implemented.

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Five Reasons why the End of Meaningful Use is the Beginning of Meaningful Digital Health Information

Background: The HITECH Act promoted the use of electronic health records (EHRs) as part of the American Recovery and Reinvestment Act of 2009.  Meaningful Use is an incentive program for providers to adopt EHRs.  The MU program had built-in (and much debated) financial incentives and penalties. Requirements for MU include mandates and regulations regarding what must be in an EHR.  These regulatory requirements and EHRs which were designed around them are what have led to overwhelming provider dissatisfaction with EHRs.  An excellent music video on the subject describes this. Andy Slavitt, the acting CMS Administrator announced last month the end of Meaningful Use. This does not end government’s interest in health information technology, he suggests, but (hopefully) shifts focus from implementation to improvement.  In a CMS Blog piece  Slavitt elaborates:

“…For one, the focus will move away from rewarding providers for the use of technology and towards the outcome they achieve with their patients.

Second, providers will be able to customize their goals so tech companies can build around the individual practice needs, not the needs of the government. Technology must be user-centered and support physicians, not distract them.

Third, one way to aid this is by leveling the technology playing field for start-ups and new entrants. We are requiring open APIs so the physician desktop can be opened up, moving away from the lock that early EHR decisions placed on physician organizations, to allow apps, analytic tools, and connected technologies to get data in and out of an EHR securely.

And finally, we are deadly serious about interoperability. We will begin initiatives in collaboration with physicians and consumers toward pointing technology to fill critical use cases like closing referral loops and engaging a patient in their care. And technology companies that look for ways to practice “data blocking” in opposition to new regulations will find that it won’t be tolerated…”


I will describe five reasons why true ‘meaningful use’ of EHRs might take place because of the demise of the MU incentive program.

  1. The end of deadlines and incentives will hopefully signal the focus of IT on the patient. Meeting MU compliance deadlines has been one of the main priorities for CIOs. With this pressure gone, we can hopefully see their attention expanded to important issues like deployment of analytics for population health management and the adoption of mobile health technologies.
  2. EHRs will be designed for clinicians not administrators. The ability to have the EHR contain more relevant and easy to find clinical data instead of billing and other administrative data is what providers have been clamoring for.  EHR vendors without MU requirements can easily create interfaces designed for the type of provider in mind (generalist, type of specialist) and even make the record reflect the individual clinician’s needs.
  3. Connected Health. Mr. Savitt mentioned opening up EHRs to connected health technologies. Personalized medicine can only occur if data specific to an individual (from biosensors, DNA analysis, and other patient-derived data) is able to find its way easily to the EHR. Heretofore this has been difficult for some and impossible for others, leaving patients and providers at the mercy of the EHR vendor. Opening the EHR to new technologies will go a long way to getting healthcare where many visionary patient advocates would like it to go.
  4. Real patient portals. The implementation of patient portals was part of Stage 2 of the Meaningful Use program. The MU participatory requirements for patient portal use were extremely low. In addition, the extent of a patient’s access to data is very limited. Patients deserve more access to their own data.
  5. ? The Holy Grail: True interoperability. True interoperability as defined by HIMSS is “… the extent to which systems and devices can exchange data, and interpret that shared data. For two systems to be interoperable, they must be able to exchange data and subsequently present that data such that it can be understood by a user.” Having a patient’s record sent seamlessly electronically from one provider to another or from any testing to a provider is essentially what providers and patients hoped for with the widespread adoption of EHRs. Some say it will never happen. One excellent overview of Health Information Exchanges discusses the challenges ahead.


I certainly don’t expect the end of MU to lead to the demise of EHRs as occurred in the NHS of Britain. One positive outcome of MU was the widespread adoption of EHRs. At the same time, the EHR became the face of all of digital health for most providers, possibly making the adoption of other digital tools more difficult. In the end, the MU carrot was more of a stick.  MU was designed with regulators at its center and not patients. If a technology or strategy in digital health (or anything in healthcare for that matter) is designed with the patient in mind, it has a much better chance of succeeding.

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Five Ways Rare Diseases can Benefit from Digital Health Technology

Rare diseases present special challenges for patients, families and clinicians alike. Symptoms may be mistaken or assumed to be more common ailments. There is not enough searchable material online for lay people, as most of the data and information might be found only in medical journals which are expensive to obtain and hard to understand. Once diagnosed and reviewed by an expert, the disease might still be so rare that opinions from multiple expert/researchers are necessary- a scenario I have just encountered with a family member. A real-time registry technology utilizing the features described below represents in my view the ideal tool for rare diseases.

  1. Longitudinal studies. These are research methods in which the same people are studied over long periods of time. Advantages of this study design include the opportunity to examine natural histories of human behavior, disease emergence and progression, and the effects of various interventions over years and decades. Perhaps the most famous and impactful study of this type is the Framingham Heart Study which began studying a local population in Massachusetts in 1948 to determine risk factors for cardiovascular diseases. Much of our understanding of these risk factors is rooted in this study which continues even today. In 2002 the study began examining the third generation of the original participants! Studies of this nature are critical in understanding rare disease which require data pooled from as many patients as possible. What this study proved is that longitudinal studies are feasible over many decades and they can produce impactful results from accumulated data. Digital health tools today have the ability to collate data from huge patient populations real-time. In doing so, they can even result in the discovery of rare diseases This data needs to incorporate subjective data (via patient survey apps), remote patient monitoring data, and data derived from social media sites. In this way, a complete picture of the emerging disease can be painted.
  2. There is no bigger waste than important data which is stagnant, undiscoverable, or both. Analytics allow for any question to be answered when throwing a large data collection net out. The importance of this model is amplified with rare diseases. Analytics bring data to life because it is presented in useful and understandable ways and can, with some technologies, be customized spontaneously with regards to type of data collected or presentation. I’ve previously written about how analytics will change healthcare. The combination of longitudinal studies and analytics are very powerful and will result in patient registry data, health policy changes, and new treatment strategies. Those who embrace analytics most are payer-providers (Kaiser Permanente and Geisinger) and ACOs who appreciate the ROI of technology in patient outcomes and thus cost-savings. They have demonstrated the value of analytics. These same analytics can be made available in an open platform to providers, patients, and caregivers so that they might share this information and make adjustments in care, daily life, and life planning.
  3. Incorporation of genomics in registries. Genomics has played an important role in the discovery and ongoing expansion of rare disease knowledge bases. The US Department of Health and Human Services has a Genetics and Rare Diseases Information Center for the public and has established the Rare Disease Clinical Research Network which has over 20 rare disease research consortia. The need to directly incorporate genomics data into individual patient EHR records and portals is critical. This will facilitate the sharing of the complete array of data pertinent to these patients among providers and experts.
  4. Caregiver involvement. The proliferation and success of documentaries and films about rare diseases speaks to the human experience germane to all patients and caregivers in the arena of rare diseases. Frustration over the lack of treatments borne out of the lack of knowledge is a common thread. Many of these films demonstrate the importance of the shared experience. Social media groups in healthcare are proving essential tools in the absorption of information among caregivers. There are many benefits of online support groups and they are magnified when applied to those affected by rare diseases. Also prominent in the films is the benefit of seeking out experts with the most experience in treating the disorder. However, most often these experts are not encountered because of financial restraints (insurance payment, logistics) or the lack of knowledge of the local physician.
  5. Comprehensive communications and monitoring tools. Digital communication can take many forms. Firstly, it can involve the initial transmission of data or teleconference between a referring physician to an expert. It can also include messaging of symptom status with transmission of remote monitoring data between patients and clinicians, updates of developments in research or observations among researchers, or the exchange of messages among caregivers. Registry platforms with the ability to facilitate communications along with data including genomics are on the wish list for clinicians, geneticists, governmental agencies, patients and caregivers.

Rare diseases present many problems to patients and caregivers along the entire journey from pre-diagnosis to referral to an expert, to determining best care. The best care might not be yet determined because of the rarity of the disorder. However, a diagnosis and evaluation by an expert are critical. Registries if done properly can address all of these challenges as well as aiding the experts in determining best treatment. The technology is here. Let’s use it.

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Five Reasons Why Population Health Management Matters

The term Population Health has replaced patient engagement as the latest buzzwords in healthcare. There are a few reasons for this. It is a necessary evolutionary strategy born out of the worsening epidemic of chronic diseases (obesity, diabetes, hypertension, and others). It is also the foundation of a new payment model.  Population health is best defined in an article in the American Journal of Public Health by Kindig and Stoddart as “…the health outcomes of a group of individuals, including the distribution of such outcomes within the group…The field of population health includes health outcomes, patterns of health determinants, and policies and interventions that link these two.”  Of related importance is addressing disparities in healthcare as contributors to outcomes. As patient outcomes become a metric for value based healthcare payment models, the focus of providers is shifting to preventive medicine, less testing and procedures, and helping people manage their own lifestyles as much as their chronic condition care. Addressing epidemic levels of chronic diseases as a matter of public health (as was done in past centuries in efforts to address infectious diseases) may seem appropriate on the surface, but presents problems concerning civil rights and other regulatory bodies.

  1. It is about people. An interesting overview of the potential roles of the Center for Medicaid and Medicare Services (CMS) in population health management (PHM) is given in an article in the New England Journal of Medicine. It describes the importance of healthcare between visits to providers, issues around provider scope of practice, necessary partnerships with private industry, and other issues. The article conveys a realistic assessment of the magnitude of the challenges involved in such an undertaking. It will not be something addressed with a single program, entity, or technology. A population is composed of individuals. Many will share common chronic diseases for which treatment guidelines are in place. Others will be at high risk of developing these chronic diseases. Within these groups lie factors which cannot be approached in a cookie cutter fashion.
  2. It creates a new and questionable way of paying for healthcare services. While population health outcomes are a laudable goal of payment models, there are limitations to what an individual healthcare provider can accomplish. One cannot force a patient’s change lifestyles or medical regimen adherence. Tools which improve adherence and lifestyle can be ‘prescribed’ but many need to be proven and adoption will take years before a (potentially) positive financial impact is felt, for which payment models should take a huge back seat to patient outcomes as a focus of PHM. What does not need to be proven are benefits of changes in lifestyle (which can be facilitated with motivational messages, coaching, and financial incentives). As with all policy changes, the devil is always in the details. Will a physician in a solo or small private practice who is exempt from having electronic health records be held to the same metric standards of PHM as a large physician ACO? Will physicians in geographical areas of high and low rates of obesity or significantly diverse ethnicities be held to the same outcomes metrics? Questions remain as to whether PHM will actually change the delivery of care itself by physicians. According to a report by The Rand Corporation the implementation of other newer health care payment models have resulted in huge increases in non-clinical administrative burdens without a significant change in face to face care changes. I would submit that better access to relevant data (via excellent analytical tools) reviewed and managed by informaticists (recently approved as an internal medicine subspecialty) with technology providing accurate and filtered actionable data is a better way to effect change in care. The implementation of deep and customizable (according to clinical profiles, geography, genetics) patient population registries with analytics and EHR interoperability seems to be an appropriate first step.
  3. It is a potential equalizer for providers and patients. EHRs were touted to decrease divides among populations by providing data which can result in the delivery of more equitable care. This hasn’t happened because of the inability of EHRs to collect data from multiple disparate sources, facilitate data searches, provide good analytics and to proscribe strategies for PHM. EHRs have the potential to improve patient care if improvements in the user experience of providers via easy to use interfaces and customizable data collection and analysis take place. Patients on the other hand desire, deserve and are not offered portals with good visuals, are mobile, and provide information which they feel will be useful to their clinicians. Patient use of portals presently has been sadly predominantly limited to encounters of minimal quality meeting Meaningful Use criteria which enable providers to receive incentive payments.
  4. It provides a new focus for innovation and investment. As with all new initiatives in healthcare, PMH presents opportunities for innovation and thus investment in products and services designed to provide the necessary infrastructure and tools to support it. Adoption of tools which provide a perspective (via good registry and analytics) on what is ‘going on out there’ (outside of the enterprise) as well as others connecting the public as consumers and patients to providers is the minimum goal we need to first achieve. According to the 2015 HIMSS Leadership Survey, 38% of respondents said they had PHM tools in place and 51% said their organization improved population health based on IT tools. Two-thirds stated that their organization was increasing its IT budget this year. However, according to a KPMG poll, 38% of respondents described their PHM capabilities as in their “infancy.” Investment by enterprises in analytics is critical in these efforts. The technology is here. Putting the pieces together (see below) and adopting them involve shifting cultural, economic, and internal political forces.
  5. It requires a multidiscipline team and portfolio of technologies. There is no single organizational department, process or technology which can address PHM. The varied needs of the spectrum of individuals in a population and requirements of different enterprises necessitate diverse strategies, goals, and utilization of human and material resources. As stated previously on this site, technology is not a solution but only becomes such when incorporated into processes and human workflows which accommodate it.  Predictive analytics,  proscriptive analytics,  excellent remote patient monitoring tools,  customized and EHR-integrated connected clinical business intelligence,* and intuitive user interfaces* all provide elements necessary for successful PHM. Partnerships among technology vendors, public and private healthcare stakeholder sectors, and between patient advocacy and provider groups need to occur for success. It will take investment and creative strategies to design the most economical,efficient and effective PHM initiatives.

The culture of healthcare on the part of patients and providers must change. Transformation needs to occur more quickly than regulators expect from changing payment models. There is a stellar quality of leaders already in this field. They must be given the political clout and technology tools to achieve those goals because clinicians and patients will not tolerate the status quo of the 15 minute encounter for much longer. The goals of population health management, if focused on the people and not regulations, commercial successes of vendors, or payer subscriber levels, can be met in some significant degree.

*As disclosure, the author serves as an advisor to  Medivu.

Posted in analytics, communications, digital health, digital health technology, Healthcare IT, medical apps, medical devices, mobile health, patient advocacy, remote patient monitoring | Tagged , , , , , , , , , , , , | 3 Comments

Five Requirements for the Adoption of Genomics as a Digital Health Tool

That digital health technologies will be adopted to address the ills of healthcare systems around the world is a foregone conclusion. The challenges of the cost of chronic disease burden, physician and patient dissatisfaction While awareness of digital health is being created with technologies like patient scheduling apps and telehealth video conferencing, a potentially more profound game changer, genomics is rarely injected into the conversation. The potential benefits of genomics lie in areas of both public health and disease treatment.  Before genomics becomes an integral part of population health management, there remain fundamental prerequisites yet to be realized. As a disclaimer I have no financial interest in any commercial entity this post mentions or links to.

  1. Education of providers. There are only approximately 4000 (Nov. 2014) certified genetic counselors in the USA. A recent study reveals that educating medical students in genomics is woefully lacking. Only 26% of schools in the USA and Canada teach genomics during clinical training years. An interesting study examining awareness of direct to consumer genetic testing found very few primary care physicians knew about and/or were prepared to discuss it. While physicians know the potential benefits of genetic testing for some diseases, they might not be in favor of it to the point of having their own genome determined.
  2. Awareness by ALL healthcare stakeholders. There is lively debate over payment for genetic testing. While there is widespread consensus with regards to testing of certain cancers, there are many issues to be considered with general screening. The company 23andme was stopped by the FDA two years ago from providing health reports based on genetic testing it did for consumers. However, it has found new success partnering with and pharmaceutical companies Pfizer and   Genetech. Although it had to shift business models, the company is bringing awareness via these other channels to multiple healthcare stakeholders.  This is yet another illustration of patients as consumers.   Patients should also be aware that the lack of identification of a sought-after gene of a disease might not be an absolute negative test. The disease (or predisposition thereof) might be on an heretofore undiscovered associated gene.
  3. Scaling the technology. This is not as much a function of the technology itself as it is of awareness and identifying opportunities for the clinical utility of derived data. The day when genomic analysis is done at home and applied in a meaningful way is going to come. Information garnered from such testing not requiring a genetic counselor might consist of a recommendation to discuss with a physician dosing or other changes of a medication regimen. It might be a virtual assistant suggesting that a family member being screened. Scaling this type of technology can result in targeting certain patient populations for new treatments (the consumer can opt-in or out for these protocols) via widespread sources such as social media or EHR charts.
  4. Development of tools and solutions. There are many potential clinical spokes emanating from the genomic wheel. Consider the alteration of genes of other species to facilitate organ donation or drug development based on population genetics.  Pharmacogenomics  involves the identification of biomarkers which can predict non-responders to medications and people prone to adverse reactions.  Implications of these biomarkers have made their way to drug labeling by the FDA.  The use of EHR-derived data for applications related to genomics is intriguing. Advantages of EHR-derived data for genetic research include the magnitude of data, low-cost, and the ability to follow it over time.
  5. Policies and regulations preventing abuse. It is natural to think that there is potential for discrimination against someone due to a genetic test resulting in the identification of genes diagnostic of or having the potential for developing a disease or significant clinical abnormality. What comes to mind is a potentially lethal or mental  illness. This issue has been addressed to some degree for some time now. The government has, via the Genetic Information Nondiscrimination Act of 2008 made it illegal to discriminate against a person in the areas of health insurance and employment based on findings from a genetic test. However, there remain other instances of potential discrimination. Consider life insurance, long-term care insurance or disability insurance which are not covered by the GINA. In addition, there are other drawbacks to undergoing genetic testing to consider.

Genomics is now on the national radar and was recognized in a Presidential speech earlier this year. The potential for huge strides in early disease (or pre-disease state) identification and treatments is clear. Challenges remain in multiple arenas, but I consider the biggest ones the shortage of genetic counselors, insurance payment issues (I know of insurance companies who require a genetic test to qualify patients for a new cholesterol-lowering medication but won’t pay for the test!), and need for more education/awareness of all healthcare stakeholders. I look forward to genomics becoming a larger part of medical education, water cooler talk, and the investment landscape in digital health!

Posted in clinical trials, digital health, digital health technology, education, FDA, Healthcare IT, informatics, medical apps, medical devices, mHealth, mobile health, pharma, remote patient monitoring, technology | Tagged , , , , , , , , , , , , , | 4 Comments