Opioid Drug Addiction: How Digital Health Technologies Can Help

After a hiatus I am returning to my beloved Digital Health Corner.  I appreciate all my loyal followers and have attempted to bridge my absence with posts in other social media.

In a previous post I discussed important public health issues that digital technology can address. Among them is drug addiction. We are all aware of the opioid epidemic and the human destruction in its wake.  However statistics have an impact that reaches beyond qualitative descriptions.  According to the National Institute on Drug Abuse (NIDA) from 2001 to 2014, deaths due to overdoses in the USA were noted as:

  • 8 fold increase due to prescription drugs
  • 4 fold increase due to prescription opioids
  • 6 fold increase due to heroin
  • 5 fold increase due to benzodiazepines
  • 42% increase due to cocaine

Tragic personal narratives provide a human fabric to the now commonplace trumpeting of generic news stories about the extent of the drug problem.  An important keystone for any healthcare strategy for chronic disease is patient self-management.  Giving patients tools to support them in their journey of recovery is imperative.  Digital technology use in the addiction space is not a new concept. The use of computer-based programs for addiction prevention and treatment was described as far back as ten years ago. The creation of virtual resources for information, guidance and support is necessary to addicts who are, for the most part digitally connected. I will touch on some potential applications of digital technologies to all relevant stakeholders.

The NIDA of the NIH  has a group of evidence-based screening tools for adolescents and adults to be used by health professionals. Notwithstanding this, many healthcare providers fail to utilize evidence-based treatments because they are not trained to do so. Technology can become a vehicle used to bridge this knowledge gap while also addressing logistical and economic challenges in drug treatment. Digital resources for families and caregivers are necessary as well.

There are various types of tech-assisted treatments. One is known as the Technology Educational System which is a multimedia approach focused on behavioral training. Another such program is the CBT4CBT (Computer-Based Training for Cognitive Behavioral Therapy).  Studies of TES and CBT4CBT on outcomes and other endpoints are favorable. A nice summary of the studies by the NIDA discusses the impressive positive effects on abstinence.

The classification of substance abuse as a chronic condition by the ACA has implications with regards to digital health. It has spawned interest in telehealth as a treatment tool. There are medication assisted treatment (MATx) mobile apps to guide physicians in utilizing pharmaceutical drugs to treat addiction. It includes prescribing guides, clinical support information, and contact resources.

Drug addiction affects all age groups. Digital technology, especially mobile technology is not something to be used in a vacuum in this clinical setting. There are significant physical and mental health issues associated with opioid substance abuse which require human intervention, at times on a frequent outpatient or inpatient basis. However, as supportive mechanisms for providers, patients, and caregivers, it can facilitate better care, communication, and education. As in other areas of healthcare, the question as to which publicly available mobile apps are safe, useful, and supported by healthcare professionals is always present. This is, as readers of this blog know well, a topic for other discussions. The toll of opioid addiction on Society, the prohibitive cost of rehab, and shortage of mental health personnel and addiction specialists mandates a call to arms for quality accessible and effective mobile tools. I look forward to all the brilliant minds in digital health and those whose lives have been affected by addiction to step forward and not wait for a government ‘fix’ to this problem.

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Five Public Health Needs for Digital Health Technology


Digital health technology has seen an incredible growth in the last few years, fueled by a combination of consumerization of wearable technologies,  ubiquity of mobile devices, proliferation of technology incubators, attention by government health and regulatory agencies and involvement of large companies heretofore not focused on healthcare. The fastest path to widespread adoption of these technologies is the application to the most pressing public health needs.  The mission of public health is to improve the public health and achieve equity in health status. A review of studies of text messaging for health demonstrates a general positive effect on health-related behavior. These findings, as well as growing demand for more economical, convenient, and outcome-oriented data-focused care are driving interest in digital health tools.

A recent report on public health state by state by the Trust for America’s Health describes the dilemma of increased general healthcare spending and increased public health needs with their associated decreased funding allocations. I would like to highlight what I see as public health issues ripe for digital health technologies, some of which have already spurred initiatives.

  1. Mental Health. The disappointingly slow recognition of mental illness as a medical problem is compounded by the paucity of human and digital educational, and monitoring resources in the field. While there are currently some technologies focused on mental health, their scope lies more in the consumer than medical realm.  An interesting recent review of mental health mobile apps in the journal Nature discusses positive feasibility studies but a lack of robust outcomes data. In addition, some unintended consequences of at least one app were found.  Tools providing support for medication adherence, urgent virtual visits with clinicians and self-management will have maximal effects in this sector.
  2. Opioid addiction. The National Institute on Drug Abuse has long recognized the potential of mobile health technologies for its mission. The Agency for Healthcare Research and Quality is launching a three-year $19M initiative focusing on mobile apps and telehealth aids for rural physicians in efforts to treat opioid addiction.  The Substance Abuse and Mental Health Services Administration recently released a free mobile app for clinicians called MATx.  It provides information and guidance to medication assisted treatment of opioid addiction. An interesting company to watch in the sector is Pear Therapeutics which offers a patient-facing mobile app combining digital tools to be used in association with medication-associated treatment for opioid dependence (other tools in their portfolio address other disorders such as PTSD and schizophrenia).
  3. Childhood nutrition. Nutrition is an important aspect of a child’s health regardless of age.  CDC statistics on childhood obesity are sobering.  Supporting education about the problem are reports such as the WHO Guidelines for Sugars Intake and the Global Nutrition Targets which includes its Childhood Overweight Policy Brief.  Digital health technology is going to be critical to providing educational and self-management tools to young people and their parents. Digital natives are experiencing the obesity epidemic and expect digital resources to help them. One company in the forefront of this effort is My Shapers which targets children ages 6-11 years old.
  4. Adult physical inactivity. Physical inactivity is a known significant risk factor for cardiovascular diseases. I have discussed threads between consumer and traditional healthcare digital strategies in previous pieces. Front and center in these discussions are wearable sensor devices intended to promote physical activity.  Though use of activity trackers has failed in one study to result in weight loss they resulted in improved fitness, body composition, diet, and increased physical activity.  They are used by millions of people to at least remind them to be more active which itself might promote better health. Trackers are being ‘tweaked’ to improve the user experience.
  5. Asthma has been well-studied from an epidemiologic standpoint. It is the most common chronic illness in children, found in 1/10 of them in the USA with increasing numbers. It impacts society from morbidity, mortality, and financial perspectives. Allergens causing asthma are ubiquitous and have documented to be common in schools according to a study by Boston Children’s Hospital and Harvard Medical School published in JAMA Pediatrics. Interestingly mice allergens were found in 99.5% of school samples. Digital technology in the form of an allergen sensor is commercially available from Alersense (disclosure: the author is an advisor to Alersense). It allows for custom programming of allergen sensor thresholds to alert the user before symptoms may occur.

     Digital health tools are by no means meant to replace clinicians but the potential scale, convenience, support of self-management, potential effects on cost savings and patient reported outcomes are all very significant.  Public health consumes a lot of healthcare resources which as noted above are decreasing.  With an epidemic rise in chronic diseases, the need becomes magnified.  Strives must be made to demonstrate improved outcomes and provide the privacy and safety that patients demand and deserve.

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Five Ways a Good Digital Health Registry Addresses Healthcare System Needs

Registries have traditionally been viewed as static repositories of data to be reviewed in a summary fashion after a predetermined time period.  The expanding need for drilled down relevant data has led to the development of creative sophisticated data analytics technologies.  We have witnessed the transformation of registry data collection which now includes curation of relevant raw data ranging from medical images and pathology slides to genomics.  Clinical or other predetermined data relevant to a given patient can be collected real-time from anywhere the patient is seen, regardless of the data’s EHR vendor of origin. In addition, the way in which the data is viewed real-time can be customized according to the viewer’s profile.  These developments have evolved the registry into a living digital narrative. I will discuss below some of the most useful applications of such exemplary registries.

  1. Provide real-world data for regulators. The Physician Quality Reporting System (PQRS) has been used since 2015 as a metric to determine negative Medicare payment adjustments to providers who do not meet quality standards. Without getting into unnecessary detail, one of the options of providing these metrics is reporting from clinical registries.  These registries are chosen from an approved list of registries.  The FDA has chimed in with a Guidance on the Use of Real-World Data to Support Regulatory Decision-Making for Medical Devices. The FDA recognizes that real-world data “…may be of sufficient quality to help inform or augment FDA’s understanding of the benefit-risk profile of devices at various points in their life cycle…”  Registries therefore have found their strength in reflecting the real world experiences of patients and physicians.  Clinical trials by design are rigorous and closely monitored. Adverse events and effectiveness of therapy many times require longer follow-up with more real world experience (without the restrictions placed by trials) to become known.
  2. Improve patient safety. A corollary to adverse events and efficacy is patient safety. The safety of a medication or device may reflect predictable or unpredictable serious adverse events. It might be a universal issue which is discovered only after a prolonged period of exposure. Examples are found in  the implantable pacemaker and defibrillator and orthopedic device industries or long-term risk of certain medications (ex. anti-inflammatory drugs and risk of heart attack). Registries can collect post-marketing real world data and lead to discovery of patient safety issues significantly in advance of word of mouth communications which have a bias of the threshold of information transmission.
  3. Supplant claims data with more accurate and relevant data. The inferiority of administrative claims data versus clinical registry data has been known for years. Some believe that the use of the more granular ICD-10 diagnostic claims codes will close the accuracy gap between claims and clinical data. I personally do not believe claims data will ever supplant patient reported outcomes (PRO) data because PRO data reflects not only outcomes but the outcome relevant to that patient.  I have previously discussed the importance of PROs. In addition, the aggregation of raw data can even potentially affect disease treatment strategies.  Consider the hypothetical situation in which a cancer registry pairs pathology slide data with imaging, chemistry, genetic, treatment and clinical data from physical examination.  Subgroups of patients might be identified from various combinations of data which benefit from different treatments they received (or didn’t).
  4. Provide a vehicle for PRO standardization. Patient reported outcomes are the key to determining quality. The importance of PROs and their standardization are eloquently discussed in an editorial in the New England Journal of Medicine: “…Experience in other fields suggests that systematic outcomes measurement is the sine qua non of value improvement. It is also essential to all true value-based reimbursement models being discussed or implemented in health care. The lack of outcomes measurement has slowed down reimbursement reform and led to hesitancy among health care providers to embrace accountability for results…”  Data sets derived from registries or any patient portal can be used in the development of PRO standards.  In addition, the data can be compared real-time to existing international standards thereby facilitating changes in treatment strategy earlier than would otherwise be guided by government reported analytics via feedback many months later. One might hypothesize that the visualization of data relevant to them compared to others who achieved best outcomes standards by patients via self-management platform portals might affect changes.
  5. Real-world patient reported data for price support of effective new drugs.  Many specialty medications notably biologics are very expensive. They obtain FDA approval and are priced out of reach for most patients.  They are denied reimbursement by many insurance carriers either outright or via impossible barriers to overcome.  A prime example is reimbursement for new injectable cholesterol medications for refractory familial hypercholesterolemia (which is not rare with an estimated prevalence of 1:200 Americans and as high as 1:60 persons in central Pennsylvania).  Some carriers require genetic testing to document a diagnosis (though the diagnosis can be made via significantly high blood levels alone), but won’t pay for the genetic testing which costs $2-3K dollars. Would payers rather pay for heart bypass surgery? Post-marketing patient registries which provide patient reported outcomes could significantly lower drug costs by providing data showing better efficacy than the approval studies.  This might tip the benefit-cost ratio for payers to support a drug or other treatment’s cost.

As one can see, registries performed with quality technologies can have profound effects on healthcare from many standpoints. Investment in digital tools which make Big Data come alive and transform it into relevant data can be helpful to all healthcare stakeholders. One must see this as an ROI for cost savings at its finest.


As a disclosure, the author is Chief Medical Advisor at Pulse Infoframe, a data analytics and visualization company

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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.

Posted in #digitalhealth, death and dying, digital health, digital health technology, emergency medicine, health insurance, healthcare economics, Healthcare IT, medical apps, medical devices, mHealth, patient advocacy, patient engagement, remote patient monitoring, smartphone apps, technology, telehealth, Uncategorized | Tagged , , , , , , , , , , , , , | 1 Comment

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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