As leaders in the healthcare industry, providing high-quality care to our patients remains at the forefront of our missions. We strive to keep people healthy, to end needless suffering, and to treat illness. Equally as important is the experience of our clinicians and our patients, enhancing interactions through rapport building, active listening, and shared decision-making incorporated into each encounter.

Today’s world is filled with technology – it’s how we communicate in so many ways. So the question becomes – does every interaction between doctor and patient need to happen in person, in an office? Does there need to be an in-person requirement to ask a patient how they are feeling? To look them in the eyes? To listen to them? To write a prescription? The answer to this question is no, not always. But we do need to assure that the human connection is not lost when interactions are not occurring face to face.

In the United States, physicians are trained on seven core communication competencies:

(1) building the doctor-patient relationship;

(2) opening the discussion;

(3) gathering information;

(4) understanding the patient’s perspective;

(5) sharing information;

(6) reaching agreement on problems and plans; and

(7) providing closure.

We need to ensure that these core competencies are not lost. Instead, they are effectively translated to whatever modality we use to interact with patients, be it in person, through SMS, asynchronous video recordings, or remote monitoring. This will require training of clinicians and non-clinicians alike, and education in our medical schools and residency programs. We also need to acknowledge that with this new world of interaction and information collection through digital data, we must be stewards of patient privacy and security.

Take this opportunity to document and memorialize workflows and processes in your organization – leverage the progress and momentum that occurred during the COVID pandemic.

The COVID-19 pandemic accelerated a growing trend in the usage of telehealth. Seemingly overnight, telemedicine visits spiked across the nation as providers scrambled to find new methods of providing care amid the pandemic. Many organizations witnessed change spurred at unprecedented rates as healthcare staff was forced to change workflows and processes ingrained in daily operations. Years of care delivery advancements took place in only a few short months to meet our patients’ clinical, safety, and access needs. As medical offices and clinics postponed or dramatically reduced care across the country, those that adopted telehealth provided an opportunity to continue care while mitigating physical – and psychological – challenges resulting from the pandemic. 

Some do not realize that the definition of telehealth goes far beyond synchronous video or telephone visits, i.e., telemedicine, which is only the tip of the iceberg. Telehealth, or virtual care, is a broad term encompassing a wide spectrum of digital capabilities, including remote physiologic and therapeutic monitoring, e-consultations, digital check-ins, and digital therapeutics, to name a few. These options should be seen as tools that augment the care of patients – not as care alternatives or replacements for traditional care delivery. If there was any silver lining to the pandemic, it was that virtual visits should now be considered a core component of longitudinal care delivery – a routine way in which clinicians interact with their patients.

Take prenatal care, for example. The American College of Obstetrics and Gynecology supports a hybrid care model for prenatal care, where certain visits can be conducted virtually in an effort to “enhance, not replace, the current standard of care”. This reduced the burden of the traditional 11-15 in-person prenatal visits, improved efficiencies by consolidating procedure-based care in-person, and left time for education, providing guidance, and shared decision-making through a virtual visit from the comfort of a patient’s home. The addition of blood pressure and glucose remote physiologic monitoring for high-risk patients could serve as a key quality enhancement to routine prenatal care.

Implementation of hybrid models of care or the addition of new services, including remote patient monitoring, will require change management. There will be different workflows, care teams, clinicians’ expectations, reimbursement structures, and technology requirements. Clinical documentation in EHRs are typically set up for patient encounters and not asynchronous interactions. Data will need to be stored and trended, alerting a clinician only when clinically appropriate. There will be an opportunity for higher engagement with patients. Still, the role of the physician and other care team members, the frequency of digital interactions, video interactions, and in-person care will need to be better defined. We should advocate for new funding opportunities and grants to test out hybrid care programs, allowing all healthcare systems – regardless of size and resources – to take the leap into hybrid care, test, iterate, and learn. 

It’s pivotal to ensure virtual care results in equitable access and does not further the digital divide. Meeting people where they are is perhaps one of the biggest benefits provided via virtual care. A 2018 Cedars Sinai study showed that a pharmacist-led, barbershop-based medical intervention could successfully lower blood pressure in high-risk African-American men. Coupled with synchronous or asynchronous physician visits, such programs have significant potential to reach underserved communities. Programs can be simple – a connected tablet in a homeless shelter or a virtual visit “office” in a public library can allow for equitable access and support the bridging of health disparities.

As healthcare leaders in the post-COVID era, we have a great opportunity to advance the adoption of virtual care across all facets of healthcare. Take this opportunity to document and memorialize workflows and processes in your organization – leverage the progress and momentum that occurred during the COVID pandemic. Explore new opportunities for telemedicine and digital health, including emerging fields such as remote patient monitoring.  And, on a more global basis, monitor policy developments and advocate to support ongoing access to virtual care. Together, we can ensure virtual care becomes a key component of providing high-quality care to the patients we serve, wherever they are.

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Swedish Medical Center has been proud to serve as an essential member of the South Metro Denver community for more than 100 years. The hospital began in the early 20^th century with a focus on tuberculosis treatment, but our programs have grown and evolved in the ensuing years. Now a Level 1 Trauma Center and regional referral center, we serve 200,000 patients annually from across the country. A proud member of HCA Healthcare, we are committed to the care and improvement of human life. At Swedish, we understand that to achieve this mission in our increasingly digital world; we must utilize the advanced technology to improve the population’s health. And we have been doing just that by leading the way in using cutting-edge tools and technology to reach patients, regardless of physical location.

A Pioneer in Telehealth

For more than a decade, our team has been using telehealth technology to connect our experts with patients in rural and/or underserved areas. Patients in these areas have little access to the highly specialized care our team provides. We currently serve a 10-state region with 12 telehealth specialties, including telestroke. Our telestroke program is our most extensive program—we have 60 sites across a four-state region. This focus on telestroke is a natural fit for us, as we are the first comprehensive stroke center (the gold standard for stroke care) in Colorado and the Rocky Mountain Region, as named by the Joint Commission. This status recognizes that we provide the most advanced technology, highly specialized staff, and unique training – all of which comprehensive stroke centers must have to provide complex stroke care.

Pandemic Increases Demand

The recent COVID-19 pandemic has allowed us to reach even more patients using telehealth technology, providing our most vulnerable populations access to care while remaining protected at home. Prior to the pandemic, our team averaged 635 telehealth encounters per month. In April 2020, we completed over 15,000 telehealth encounters representing a more than 2,000% increase! We are so pleased to have the experience and systems in place to serve such a vast array of patients during this difficult time. We understand that people have been afraid to visit a hospital physically during the pandemic, but that doesn’t change their need for care— whether preventative, management, or acute. With such a high prevalence of smartphones and comparable technology in nearly every household, we can make this type of care widely accessible. Our doctors have shared with me time and again that telehealth has created an opportunity for more compassionate care. “Meeting” patients in their homes has removed any sense of hierarchy and further humanized care visits leaving patients more comfortable and relaxed. In some cases, we have been able to utilize a remote monitoring platform to observe patient vital signs, allowing us to assess the patient remotely and either have them proceed to the emergency room or to set up a return televisit if we are able to determine the need is not urgent.

Meeting” patients in their homes has removed any sense of hierarchy and further humanized care visits leaving patients more comfortable and relaxed.

Telehealth Saves New Mom’s Life

The statistics and outcomes we see from our telehealth efforts are exciting, but, we love to celebrate the amazing testimonials our patients share at Swedish. One of these impressive stories is from a new mom in South Dakota, Frances Mackey. Mackey was in the waiting room of Spearfish Regional Hospital, tending to her two-week-old daughter when she suffered a massive ischemic stroke. Since the Spearfish hospital is a Swedish telestroke site, the care team at Spearfish was connected with a highly experienced Swedish stroke neurologist who diagnosed the clot and coordinated her care. Mackey received tPA (tissue plasminogen activator, a stroke treatment medication) on-site at Spearfish and then airlifted to Swedish Medical Center to undergo lifesaving thrombectomy. As she told a reporter who covered the story, without the telestroke program and quick-acting doctors, she doesn’t know if she would still be alive to care for her daughter.

Reaching New Horizons in Telehealth

Stories like Mackey’s inspire us to continue to find new technology and expand our outreach programs. Less than two years ago, we began using Artificial Intelligence (AI) to synchronize stroke care. This program is built into a convenient smartphone app that allows our neurologists to review imaging in real-time on their devices. Within seconds, they can diagnose and determine if the patient should be airlifted to Swedish for further treatment or continue care at the current location. The technology reduces treatment delays, which is imperative as every second matters in stroke care.

Within the past year, we have added Telehealth ED, a program that allows emergency department providers in rural areas (often nurse practitioners or physician assistants) to consult with our board-certified emergency department physicians.

During the onset of COVID-19 pandemic, we began using a remote monitoring tool with colleagues and patients who had possible exposure to the virus. The free app is simple to use —patients receive a text message to download the app. After downloading, they answer a few health questions to screen for COVID-19 and enter information related to their symptoms. If symptoms warrant, a nurse reaches out to coordinate the next level of care.

I am excited about how future technology will allow us to serve an even more significant number of people and improve overall population’s health – not only within our Denver metro neighborhood but also throughout the Rocky Mountain region and our nation.

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Integrated health care systems are adopting digital and information technology, along with advanced analytics, as key ingredients in their transformation to population health. This translates to improved quality and reduced medical costs and will give them a strong competitive advantage.

Not only that, but the COVID-19 pandemic has accelerated the adoption of virtual care, and we can expect that the use of technology-based solutions will continue to grow. Virtual care, remote monitoring, smart wearables, smartphone apps, artificial intelligence, care management platforms, clinical decision support tools, digital therapeutics, shared decision-making, self-management education/training, care team workflow, and support, population surveillance, secure messaging, data exchange, and integrated databases coupled with advanced analytics all can be assembled into a comprehensive enterprise digital health strategy. Digital technology will improve access, make care more affordable, and enhance consumer experience and satisfaction. It will also dramatically improve health behaviors and self-care engagementleading to improved outcomes and well-being for our patients.

Digital technology will improve access, make care more affordable, and enhance consumer experience and satisfaction.

Population health transformation priorities:

• Patient engagement in health and well-being
• Clinical decision support for evidence-based care
• Care team workflow, connectivity, and use of telehealth
• Data-driven decision-making

Patient engagement in health and well being

One of the greatest population health improvement challenges is engaging high-risk chronically ill patients in improving their health behaviors and self-care management.  Tailoring patient education, communication, and training toward patients’ health literacy, ethnicity, preferences, learning styles, and personality traits can effectively engage them in positive health behaviors and better self-care. Utilizing remote monitoring, digital assessment tools, secure messaging, surveillance of symptoms/biometrics, online education, and telehealth training for self-care management can all be integrated into a comprehensive program for health coaching and chronic illness management.  When patients can easily and regularly access their care teams for information, coaching, medication adherence, therapeutic decisions, reminders/alerts, scheduling, and virtual visits, they become more engaged, improve their satisfaction/compliance, and have better health outcomes.

Clinical Decision Support for evidence-based care

Clinical Decision Support Systems (CDSS) are comprised of software tools designed to provide direct aid to clinical decision-making by matching patient-specific information to clinical knowledge bases and presenting “point of care” recommendations that aid in diagnostics, therapeutics, adherence, and clinical management. CDSS often uses web-based applications or tools embedded in electronic records and computerized provider order entry systems. They also can be administered through desktops, tablets, smartphones, remote monitoring, and care management platforms. Creating systems that promote the use of evidence-based technology therapeutics and care pathways can dramatically improve clinical effectiveness, safety, and health outcomes. The scope of functions provided by CDSS is vast, including diagnostics, safety, alarm systems, disease management, prescription drug control, shared decision-making, use of surgery/technology, etc. They can be administered using computerized alerts, reminders, guidelines, care pathways, order sets, patient reports, documentation, and workflow tools. One of the challenges is introducing CDSS in a way that increases effectiveness without creating barriers and alert fatigue or adversely effecting workload and clinical efficiency. The cost and resources required for ongoing system and content management to provide up-to-date information can be large. Transportability and interoperability across EHRs and information technology are challenging. But it is worth it; optimizing the application of evidence-based care has the opportunity to reduce costs, improve outcomes, and result in higher-value care.

Care team workflow, connectivity, and use of telehealth

Virtual care and other digital technologies have increased the opportunity to integrate and coordinate care provided by multi-disciplinary teams of health care professionals. Workflow mapping and design to facilitate the efficiency of care team models is becoming more pressing due to:  new technologies/methodologies, remote monitoring and secure messaging for chronic illness, the growing number of specialized professionals on the team, complex interdepartmental care pathways, and time/resource restraints for larger systems across large geographies. Telehealth can facilitate cross-disciplinary care team conferences with and without the patient to coordinate routine care and/or discuss complex clinical issues and agree on a comprehensive care plan that optimizes resources and the use of clinical expertise. Digital and information technology, if used well, can improve the efficiency and effectiveness of care team workflow. This is why population health care management platforms that are embedded or integrated with EHRs can be transformational. Care team function must also be intentional, with careful thought to creating measurable goals, roles, accountabilities, facilitation, leadership, training, team-building, cycles of improvement, and human behavior/dynamics.  Information sharing and transfer across the teams, clinicians, and organizations are complex and can only improve with intention, design, measurement, and feedback.  Data capture of daily assessment/biometrics, comprehensive and daily assessments, and social determinants provides more timely information to the teams. Access to mental health professionals has been a challenge for our health care system. Still, telemedicine has created virtual access to mental health professionals in rurally located emergency departments and co-located and integrated behavioral health providers and clinical pharmacists into primary care settings and care teams.  

Data-driven decision-making

Information technology and data from multiple sources, including new digital health technology, should enable a number of professionals to use advanced analytics to measure performance and share information across networks. Helping health care leaders understand the variation in cost/resource use, health outcomes, clinical quality, and care experience across multiple populations will help drive the imperative for health care redesign, use of digital health technology, and proactive care team management. Integrating information and workflows across care management teams, EHRs, providers, community resources, and Health Information Exchanges (HIEs) will improve communication, reduce inefficiencies, and promotes better knowledge and understanding. Large quantities of health care data accumulating across patients and populations coupled with new analytic approaches will provide a new generation of knowledge to address the unmet information needs of patients, clinicians, researchers, and health policymakers. New learnings from improved patient engagement, advanced clinical decision-making systems, and care team function will allow us to:  improve our abilities to track and enhance longitudinal health outcomes and patient experience; measure both direct variable and total medical costs; demonstrate sustainable value.

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The emerging COVID-19 pandemic has become a once-in-a-century challenge that has impacted society profoundly and has disrupted almost every facet of life for people around the world. The demands on the infrastructure of health information technology (Health IT) are numerous, as we learn to use the tools we have created to address the issues we are facing now.

I wanted to review three key areas where health IT is being leveraged to address several facets of the pandemic response:

1. Moving ambulatory care to an increasingly virtual environment.
2. Developing reliable regional registries to coordinate needed resources, such as testing, ventilator availability, personal protective equipment (PPE) supplies, etc.
3. And using Artificial Intelligence (AI) to analyze data already at hand to find best practices and treatments at the point of care.

Increasing use of virtual care environments

In most areas where work-from-home and social distancing have become prevalent, and especially in areas where shelter-in-place orders have been issued, the nature of ambulatory care has dramatically changed. In primary care practices, many have developed protocols that evolve day-by-day, but generally fall into the following workflow:

1. Reschedule all elective visits, such as annual wellness exams, to later in the year;
2. For those whose needs cannot be postponed, attempt to set up virtual visits;
3. For those who are unable to do virtual visits, then they are seen in the office.

Some practices, such as non-trauma orthopedics, are unable to find operating room availability, and their practices have ground to a halt. The result of this has been a dramatic and sudden decrease in office volume – 5 or 6 virtual visits in a day, plus one or two in-person visits, in the place where a practice used to be 20 visits per provider per day, is not economically sustainable, especially for small and independent practices.

Many electronic health records (EHR) systems have an added feature to do video visits, but not all do. Historically, the use of such technology has been only a trickle, primarily driven by payer coverage of such visits. In the recent times, this has changed rapidly, and payment for such visits on a par with in-office visits has removed the disincentive for practices to use such resources. Our practice participates in a network of a few thousand regional independent practices, and the use of video visits has spiked by two orders of magnitude in the first three weeks of March.

Practices that use EHRs which cannot do in-system video visits have to find separate stand-alone systems that can accomplish the task. Despite some casual comments about using Facetime and Skype for such visits, these methods are not HIPAA compliant and run the risk of HIPAA breach when used. There are other platforms, in-EHR and stand-alone, which are HIPAA compliant and should be the methods used.

With the rise in use of virtual ambulatory care, there is an increased bandwidth burden on the system. Coupled with increased bandwidth demands from households that are now stay-at-home, such as work-from-home, teleconferencing, entertainment streaming services, online shopping, etc., the bandwidth burden may result in internet slowdown. However, for ambulatory medical practices doing more video visits, there is an offset of reduced overall visit volume. The carrying capacity of the internet does not seem to have been exhausted as yet.

Regional networking to coordinate resources

In regional areas, such as the San Francisco Bay Area, efforts to coordinate resources have emerged, involving numerous stakeholders – CIOs and CMIOs at hospitals and delivery networks, and CMOs at technology companies.

Such initiatives have included developing real-time registries of availability of COVID-19 testing, given that these resources are changing day by day. Capacity issues exist, making obtaining testing materials difficult, and limited laboratory capacity in performing these tests means slowed response times for reporting results. But the supply availability and testing capacity are changing daily, so real-time, single-source-of-truth dashboards are needed, and many in technology are developing this. The needs are regional, not local, given the mix of services emerging – public health laboratories, emergency department hospitals, universities, commercial laboratories such as Quest and LabCorp, some physician offices, drive-through test options, and even the emergence of the collection at home. All these mixed efforts can be chaotic, not strategically, and efficiently deployed unless there is a regional coordinated effort. These are bottom-up initiatives, more than top-down, growing out of needs “in the trenches.”

Supply chain issues around personal protective equipment, availability of nursing resources for ventilators, the availability of ventilators in the first place, are all matters where resource coordination is similarly emerging. Local initiatives, such as converting operating rooms (since few elective surgeries are taking place) to Intensive Care Unit (ICU) beds with respiratory support, are ideas and experiences which are being shared through health IT networks.

Using AI to find best-practice treatment strategies

There is a tremendous opportunity for using AI to identify treatment options that work best. The standard of care in medicine has relied on the classic double-blind prospective study, where subjects are recruited to meet study criteria, then subjects are grouped to blindly receive either the treatment-under-study or placebo and the results followed over time. It’s a lengthy process. The spread of COVID-19, where the virus is infectious days before symptoms first appear, and which seems to be spread easily, given that it is novel and no one has immunity to it yet, makes such a process important but not fast enough.

AI looks at patterns in the data at hand. It is by nature, retrospective, and is not necessarily affected by study design biases. With sufficient data to analyze (the worldwide data is accumulating rapidly), AI can identify the risk of severe illness, and risk of death, based on observation of demographics, co-morbidities, and other medications taken. Anecdotal reports of successes can be tested – for instance, the report of the benefits of chloroquine (a malaria drug) for reducing respiratory failure risk has become a pop phenomenon. It has resulted in a run on chloroquine at local pharmacies, and an indefinite back-ordering due to supply chain and manufacturing capacity issues. Other remedies using influenza treatments or HIV treatments, alone or on a combination, have been tried, again with anecdotal reports. To make sense of all this, AI can help deliver sane, observational best-practices recommendations that can guide health care delivered at the point of care. And do it quickly.

Conclusions

The global impact of COVID-19 is unprecedented and has disrupted almost every facet of daily life. Healthcare delivery is significantly impacted in its response to this pandemic, and the health IT infrastructure is an integral part of this response. Many tools already in place, such as video visits, are ramping up in their usage at a dramatic pace. Regional registries of resources, updated on a real-time basis, are necessary for an environment that changes day by day, even hour by hour. Leveraging AI algorithms already available and applying it to data available from around the world may well result in creating science-based, consistent best practices recommendations. We have the tools, we are learning how best to use those tools, and need to ramp up quickly.

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Several industries have been transforming by the implementation of new technologies. Healthcare has already joined this race of introducing technology supported value to key players in its own domain: patients, doctors, clinics, pharmaceuticals, insurance companies, CROs, biotech companies. Oncology, the study and treatment of tumors, can be brought to next level in 3 major areas by exploring benefits of Artificial Intelligence, machine learning, automation and blockchain: Diagnosis, Decision Making & Treatment, Research.

Diagnosis

With HIPAA rules in place, patient and healthcare data cannot be shared efficiently across healthcare stakeholders. However, the Office of the National Coordinator for Health Information Technology (ONC) proposes to implement the “information blocking” prohibition of the 21st Century Cures Act which can alleviate roadblocks for transparent data share. In addition, new healthcare blockchain solutions that are emerging can solve data in transit and at rest security concerns. Still, with 23.6 million new cancer cases per year globally by 2030, this is not an answer to build efficiency with vast amount of cancer patient data: scans, radiology results, next-generation sequencing files, immunohistochemistry results, pathology analysis, lab reports, treatment information, drugs that are required to diagnose a patient. Reviewing, entering, and analyzing all this valuable data set, is an enormous uptake for nurses, and doctors.

Now by applying cognitive technologies medical information, treatment options, clinical trials can be reviewed and stored faster than any human. Machine learning can support to manage all this critical mix of data in a usable format and can help interpret the diagnosis of a patient. Unstructured data is other the part of the puzzle. Lack of standardization and rule sets across institutions result in more data integration and preparation issues to kick start AI efforts. Although, federal government has issued standards for the electronic health record (EHR) and formats such as FHIR for health care data exchange, published by HL7 seem to be in place, unexpected data gaps for basic patient information can be observed at clinical institutions due to operational inefficiencies, or lack of expertise and interest in having a robust internal clinical data.

Decision Making and Treatment

Timeliness and accuracy are key in decision making and in building a personalized treatment plan for cancer patients. Using pattern recognition in machine learning with supported or unsupported models to identify patients’ condition for probable treatment options based on improved clinical pathways or matching them to clinical trials is where we observe happening more frequently.

A more comprehensive approach for disease management, better coordinate care plans and help patients to better manage and comply with their long-term treatment roadmaps is in demand but can only be achieved with technology and clinical expertise moving along hand in hand. These implementations at hospital level integrated with EMRs would alleviate the pressure on staff and doctors, improve patient satisfaction by identifying less toxic, better performing systemic therapy solutions commingled with on the spot genomic recommendations and probably improve progression-free survival rates with decreased costs. Understanding clinical pathways, leveraging existing ontologies can speed up implementation of these AI initiatives.

Research

On average it will cost a company to develop a new cancer drug from the research lab to the patient is $650 million and it takes at least ten years for a new cancer cure to be on the shelves, with clinical trials alone taking at least 4 years. Regardless a randomized or a non-randomized clinical trial such as TAPUR which is open and enrolling patients at 113 sites, a clinical part of reach and development efforts might involve all types of drugs into account as inclusion or exclusion criteria or for intervention: chemotherapy, immunotherapy, targeted drugs applied standalone or in combination as regimens. Here, AI can be applied to drug research and discovery by streamlining the drug discovery, increased clinical trial recruitment, drug phase repurposing, population densities. Results would be observed as time to market improvement, decreased drug development costs.

For healthcare professionals, one of the main challenges is around trusting on the AI or in other words black box problem of AI. A mistake at anywhere in the chain of AI solution might result in an unwanted situation for patients. But it is possible to overcome these concerns.

In the adoption of this set of AI technologies, building a team around technological know-how is a good start but not enough. On the side of technologists who are experts on natural language processing, neural networks, variations of machine learning methodologies, phyton or R, you would need to build a multidisciplinary team with doctors, nurses, genomics experts, data scientists. Rigorous tests and validating AI based outputs by these experts would ensure more reliable results. This would have to be supported with real-world data and patient outcomes and be part of the loop to feedback into machine learning algorithms. This leads to one probable issue – too much trust in the system, as humans we get comfortable with ease. It would be wise to have a guardian of AI, reviewing the output of the algorithm at a non-stop pace.

Healthcare and cancer treatment is on the brink of change in the next 10 years thanks to AI and other innovative technologies. However, it all depends on us as in our own corporate silos or network to work in collaboration.

The post In the Search of AI for Oncology appeared first on HealthTech Magazines.

Some months ago, I read “Draft Number 4,” a collection of essays on writing by author John McPhee, a Pulitzer Prize-winning novelist and professor of literature at Princeton.  His courses on creative nonfiction are highly sought after by aspiring writers and many of his students go on to achieve fame in their own right.

Tucked away in McPhee’s book was a quote about one Sara Lippincott, a former ‘fact-checker’ and editor for the New Yorker magazine, expounding on the subject of journalistic errors.

Any error is everlasting. As Sara told the journalism students, once an error gets into print it “will live on and on in libraries carefully catalogued, scrupulously indexed . . . silicon-chipped, deceiving researcher after researcher down through the ages, all of whom will make new errors on the strength of the original errors, and so on and on into an exponential explosion of errata.”

There was a simple beauty to this warning around the permanency of errors, leading me to think about the parallels between my own profession, medicine, and journalism.  Both represent, ostensibly, a search for truth.   The journalist seeks this through questioning, research and reportage, and the physician, from the patient’s history, physical examination, laboratory work and imaging studies, among other things.  Unfortunately, we in medicine do not have the luxury of teams of fact-checkers (a vocation quite unknown to me prior to reading McPhee’s book) poring over our notes in the medical record.

Consider the state of the daily progress note.  A progress note in medicine was once a thing of simple elegance  – capturing the narrative of the encounter in addition to the plans set forth by the practitioner in what was hopefully a goal-directed manner.  The physician Lawrence Weed in 1964 proposed some additional structure, effectively dividing the narrative into Subjective, Objective, Assessment and Plan components.  Over time, the note developed additional ‘weight’, becoming tied to financial, regulatory and legal implications for the practicing clinician.  And then came the task of transitioning to the electronic medical record or EMR.

I should be clear here, this is not a diatribe justifying the return to paper medical records. I firmly believe, after having overseen multiple EMR transition events in various facilities, that healthcare is fundamentally safer and more efficient as a result.  With this transition, however, there came the following negotiation: accept and learn the process of entering orders electronically, and in return, we’ll make entering notes easier for you.  Hence the advent of cut and paste, copy forward and the subsequent ‘cloning’ of notes. And this may have inadvertently lead to an erosion of the clinical narrative.

Throughout my career, I’ve seen progress notes that detail a patient remaining intubated and sedated when they are clearly not, a plan for consultants to be notified when this already occurred days prior, as well as other myriad factual inconsistencies and error. Some EMR vendors offer mechanisms to allow for reviewers of notes to quickly identify which parts of the note have been copied forward and which parts of the note were actually generated de novo that day. Others offer a mechanism of generating footnotes each time sections of the record are cut and pasted so that the attribution of the source is always present at the end of the note.

While both of these represent valiant attempts to allow shortcuts to persist in a way that coders can readily follow, the overall readability of these notes remains compromised.  For instance, I’ve turned on the ‘cut and paste x-ray vision’ of the first example on a large, seemingly impressive note, only to find out that the actual new information shown amounted to just a few lines of text, and the vast majority of the remaining note was in fact copied forward from the previous days.  And medical progress notes with bibliographies?  That just seems like it was made with legal experts and financial coders in mind, not clinicians quickly needing to cut to the proverbial chase of the narrative.

But I am empathetic to the plight of the busy rounding clinician and understand that needing to round on a large panel of patients and then individually generate completely new narrative notes might prove simply too onerous for many.  And in a world of both physician burnout and, tragically, depression and suicide, this human implications for additional work are too real to trivialize.

So what might the solution be? And how might it capture both the nuance of the encounter that occurred that particular day, but also offer the information from previous days?  Chapter 27 of Dr. Robert Wachter’s New York Times bestseller, “The Digital Doctor,” offers a tantalizing glimpse of what the future may hold.  In a nutshell, he speaks of the encounter being securely captured in its entirety, using a combination of natural language processing and video archival to capture and document the essential elements.  This then generates a narrative note with the remaining various pieces of objective data (labs, pathology and imaging) available for review via hyperlinks. Of course, I’ve oversimplified this, but you get the idea.  The visit, using technology, becomes the note, allowing the doctor to focus on doctoring.

Regardless of the overall end-result, perhaps the first task is to recognize that the EMR is just a tool.  And like any tool, it can be used non-judiciously. Secondly, we must recognize that there may be a problem with the accuracy of our documentation, and be a participant in the engineering of solutions for it. Indeed we have done it before, weathering political, regulatory and financial upheaval in the name of continuing to take care of our patients.  We will reclaim the precious sacred few moments with our patients that is the bedside visit, and bend technology around it accordingly to capture it effectively.

Until we are able to document at the speed of thought, our charge remains being the ever-vigilant fact-checkers of our own writing. By seeking to improve the clinical narrative of every note, we also strive, much like our colleagues in journalism do, for the truth.

The post Searching for Truth in the Humble Progress Note appeared first on HealthTech Magazines.

By Elise Singer, MD MBA, Chief Medical Officer, CareLinx

Millions of our declining elderly need help in the home and 90% want to remain home to the end of life

According to Health Affairs, nine million community-dwelling Medicare beneficiaries—about one-fifth of all beneficiaries—have serious physical or cognitive limitations and require long-term services and supports (LTSS) that are not covered by Medicare. Nearly all have chronic conditions that require ongoing medical attention, including three-fourths who have three or more chronic conditions and are high-need, high-risk users of Medicare-covered services.

Unless the elderly are in poverty the government offers no in-home aid. Medicaid covers LTSS for very low-income Medicare beneficiaries, but only one-fourth of Medicare beneficiaries with serious physical or cognitive limitations are covered by Medicaid.

There is a known correlation between avoidable high medical spend in the elderly with significant cognitive and physical limitations. This gap in Medicare services leads to avoidable emergency department visits and hospitalizations and increases the risk of older adults’ ability to live independently leading to institutionalization in long-stay nursing facilities, which is paid by Medicaid. Consequently, costs often eventually simply swap government spending buckets.

Importantly, 90% of patients also prefer to remain at home through the end of life.

It’s complicated

It seems straightforward, if our health system could provide affordable supportive care in the home through non-skilled caregivers who also are enabled to pass on targeted, actionable information vital to optimal medical care, everyone would win. The elderly could remain at home and be happier, their health outcomes would improve and there would be fewer avoidable ED visits and hospitalizations. Fewer admissions provide governmental savings, whether Medicare or Medicaid, offsetting the cost of non-skilled caregiver services.

However, it is not straightforward. First, we will need to have 3 key components in place nationally: appropriate payment channels and incentives, system preparedness and a sufficient workforce.

Payment

Needless to say, payment in health care is complicated by 3^rd party payors, governmental involvement, lack of transparency due to contractual obligations and multiple key providers along the continuum of healthcare settings, and realities created by historical developments. Nevertheless, there are new federal rules expanding the definition of “primary health-related” benefits for Medicare Advantage plans beginning in 2019 that can allow for the coverage of the cost of non-skilled in-home caregivers.

The system

The most underrecognized complication is our healthcare system readiness itself.

Healthcare is like air travel at the busiest world airports. Almost around the clock, there are airplanes arriving and leaving every minute, a multitude of airlines, different languages spoken by pilots, 10s of runways, and unpredictable variables like weather. And mistakes can quickly lead to death. The right information at the right moment must be delivered to the right person. It works through a well-defined and coordinated interplay of highly educated people making decisions based on information given to them real time by technology systems crunching vast data sets based on algorithms and prioritizations. The system has been built over decades, iterated on for efficiency and has many interlocking systems and dependencies.

Healthcare systems are also a well-defined and coordinated interplay of highly educated people making decisions based on information presented to them real time by a system of integrated technology systems. The system is simultaneously efficient and complicated.

It is clear that doubling the incoming flight volume or adding in helicopters, that have a completely different landing pattern, couldn’t be successfully accomplished without significant planning. The systems need to accept or integrate the new data stream and the air controllers need to know what information is relevant and how to react based on that information.

Today, in healthcare, 95+% of a patient’s time is spent at home, or outside of medical interactions. Yet there is little data from this “home setting” to the healthcare systems or professionals. With a few exceptions, all of health care data in existence today comes through outpatient physician visits punctuated by data from high acuity settings or through informal means. Some patient-reported data from the home setting is occasionally shared, typically in paper format during a medical appointment on paper or visually noted by the nurse or doctor from the patient’s smartphone. This data doesn’t reliably enter the medical record in a discrete usable fashion.

In order for data from the “home setting” to be ingested by the healthcare system, it must be relevant, dependably accurate and timely and the health system must be able and prepared to receive it.

Workforce

Lastly, we need a sufficient supply of trustworthy in-home workers along with visibility into their hours and actual work. Issues of worker availability, accountability and reliability as well as dependable and defined mechanisms to guard against fraud and abuse are critical. Sufficient wage levels according to local markets are critical to maintaining this workforce.

The next step

Extending health care into the home via connected caregivers should be a national priority. Huge majorities of people strongly prefer to age in place, there isn’t sufficient national infrastructure to institutionalize seniors in need of functional support and it is unsustainable financially privately and publicly. Perhaps most importantly, health outcomes could improve while overall health spending decreased: connected caregivers can improve preventive care in the home through scripted prompts, provide thoughtful functional support in the home and send real-time medical data that is monitored by skilled healthcare resources leading to timely intervention opportunities and fewer avoidable hospital admissions.

With the 2018 Medicare Advantage programmatic supplemental benefit expansion, the necessary payment reform has taken an important step forward. When offered to the appropriate patient segments coupled with proper pricing, functional caregiver support tied into the medical system should be expanded to the entire Medicare population.

The caregiver workforce is readily available in most parts of the country, whether engaged informally or formally. Several franchised businesses and a marketplace offer successful channels to support an expanded need for in-home support.

In order to accelerate the health system readiness process, “connected caregiver” service lines in existence today offer a supply of high quality, trained caregivers coupled with a HIPAA compliant technology platform built for this use that can integrate with hospital and payor technology systems.

It is time to truly extend health care into the home through an affordable connected caregiver workforce deeply integrated into our medical system.

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The emergency department is, for better or worse, the front door to the hospital. With this responsibility as a first line of defense against any number of incoming cases, the department needs to be ready for anything. Yet, in today’s hospital environment, the systemic crises facing emergency departments are real and growing.

Many of the problems facing EDs today are related to volume — resources are often strained by both the overflow of incoming patients, and by understaffed providers. It has even become commonplace in overflowing emergency rooms that some patients are admitted and kept in hallways for hours or days (boarding), which can be harmful or even fatal for patients. Something needs to change; and in today’s world, solutions are often found in new technology.

It’s worth noting that the value of new technology is not in the use of tech itself, but rather in how technology enables better, more efficient and more effective practice of medicine. One technology that is driving change in EDs is the rising use of telemedicine.

The traditional view on telemedicine is of remote clinicians contracted to provide phone-based consultations with patients and other physicians. These kinds of telephone-based interactions do still occur, but with the rise of video technology, and with a more commonplace acceptance of video-based communication as a norm today, the very nature of telemedicine has expanded.

And with that expansion in scope of use and capability, telemedicine becomes a solution for staffing in overcrowded emergency departments. Telemedicine can reduce ED response times greatly, can improve the coverage across specialty areas, can lower costs and provide a boost to the bottom line economics of the department, and can by its very nature share digitized health data collected in the service of patients.

Take for example stroke care in the emergency department. When a patient begins having a stroke, the first thing they need to do is activate the 911 system. There are communications technologies and imaging technologies available in some EMS systems to begin the early stroke care right in the back of the ambulance using live video, live audio, and (in a few cases) a CT scanner deployed in the vehicle.

When that patient arrives at the hospital, the decisions to be made for that patient are potentially complex and involve a thrombolytic drug called tPA, and the patient’s potential to undergo endovascular therapy to extract the clot. While we cannot do an extraction by telemedicine, we certainly can provide the neurological expertise surrounding the drug tPA along with the expertise needed to assess patients for other therapies, including endovascular clot retrieval.

In other words, perhaps telemedicine has finally become just medicine

Telemedicine allows us to immediately have a neurologist at the patient’s bedside, at any hospital in the country within seconds, with real-time video, audio, the ability to share and review images, and to interact with the patient all in the name of making risk-based decisions about patients’ care.

This kind of on-demand expertise is now widely accepted as part of guideline-based care. And it cuts both ways, too. Recent data shows that the stroke-specialized neurologists working in telemedicine may actually provide better accuracy in treatment. Telemedicine-focused neurologists prescribe tPA in 20-25 percent of cases, while the national average is five percent. Despite this, telemedicine-focused neurologists have a rate of symptomatic intracranial hemorrhage rate at half of the national average. It’s accuracy of tPA utilization driving these trends, and that accuracy born of scale: These neurologists, by the nature of telemedicine, are seeing and treating more strokes in a month than the average emergency department would in a year.

Another way technology is unburdening busy EDs is in lightening the load of behavioral health patients seeking treatment in emergency departments. The number of behavioral health emergency department visits continues to rise, and patients can spend hours waiting for a psychiatric evaluation. Many providers are scrambling to find more cost-effective, timely treatments for these types of patient. By deploying a telepsychiatry program, providers can help lighten the behavioral health load in the ED and provide accurate, cost-effective mental health services on-demand.

In both of these cases, and in many other examples, telemedicine is decreasing improper admissions and readmissions, saving provider costs and only allocating resources where they are needed most. So, what then, is holding hospitals back from buying into it? Perhaps simply the belief that telemedicine takes away from the basic doctor-patient relationship; a belief that is statistically unfounded and in the age of modern technology, perhaps best viewed as antiquated.

Telemedicine and virtual hospitals are set to be the new norm in care. There is an old story about the early adoption of the stethoscope: Many in the medical community feared that the device would not be well-accepted because it broke from the tradition of listening to a patient’s chest directly with the ear, and it was thought that a listening device would serve to separate physician from the patient, or somehow take away from that basic relationship.

Over time though, it became clear that the device improved outcomes, and it became a generally accepted practice. Telemedicine, in this new technological era, is much the same as the stethoscope of old.

There are those that say ‘Well, this just disrupts the physician patient relationship in a way that will never be accepted.’ Yet, the data suggests that not only are these methods accepted, but in fact, in many cases they perform even better.  We’ve entered the era where perhaps telemedicine can become more than a temporary solution to demand, but more of an integrated part of the continuum of care in the ED. In other words, perhaps telemedicine has finally become just medicine.

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By Creagh Milford, DO, MPH, CMO,  Healthcare Highways

The Democratization of Medicine

Physicians, like other professionals in this Era of Amazon reviews, are finding themselves inundated with questions generated by the wealth of information available to patients via internet searches, wearables, and self-diagnosis applications, losing control of an age-old process to discover a patient’s diagnosis. Doctors will experience more patients presenting them with self-diagnosis, setting appointments for confirmation or rebuttal of their own data-driven conclusions; and doctors will not always maintain control over initiating the “art” of diagnosis. Increasingly, they find themselves in the middle of a process that once began in their office.

Smart technology, wearable health monitoring devices, and the software connecting them—the Internet of Things—has established greater expectations of personalization, customization, and timeliness in healthcare treatment. The current care model is slow to embrace the patient as partner in his or her health management.

Embracing these technologies will bring the Industry closer to the quality of consumer experiences set by the exponential growth of remote, internet-based services automating, regulating—even gamifying—increasingly intimate aspects of everyday life. Everything from the purchase of food and diet programs (HelloFresh) to our emotional health can be ordered, scheduled, and delivered to our door.

Wearables and The Patient-Partner

More than trendy lifestyle badges, wearable health monitoring devices (Fitbit; Leaf) provide patients with the ability to monitor, document, and analyze their health data before entering a waiting room. Tracking and analyzing health data now occurs autonomously across an endless variety of customizable smartphone applications.

These devices offer an incredible opportunity for the early detection–and possible prevention—of diseases when integrated into incentive programs like employer wellness programs. These programs utilize remote monitoring technology and applications that collect and integrate personal health data. As care management applications become more ubiquitous and integrated into patient’s lives, a new world of automated data monitoring, collection, and analysis possibilities will open. Over time, care teams will use this data to manage larger populations (panel sizes), through management by exception rule-based logic and artificial intelligence.

Redefining Quality in Healthcare

Since 2010, the term “healthcare value” has been defined as quality delivered, divided by the cost required to deliver the unit of care. Since then the progression of population health management tactics has taken hold, coupled with more informed and consumer-driven patients, requiring a re-evaluation of our definition of healthcare value. Consumer experience has become a critical element in patients’ perception of high value healthcare. In addition, experts now acknowledge that to reduce utilization, we must also hold providers accountable for the appropriateness of tests, labs, and procedures they perform.

Amazon overcame several long-standing industry giants by its singular focus on and commitment to providing high-quality customer experiences. Healthcare, is not immune to the “Amazon Effect,” wherein companies must adapt, abandon, or strengthen their current operating models or become obsolete (Toys R Us). Amazon’s “one touch purchase” experience has become the default customer experience standard to measure against.

Likewise, if patient experience becomes the metric by which hospitals, staff, service providers, and insurers measure the quality of their care—the value and experience it provides to patients—then reduction of inappropriate care and procedures and the targeted application of procedures most likely to produce positive results take top priority.

Eliminating the current barriers between specialists, hospitals, and primary care doctors; managing the coordination of data collection, analysis, and execution; and establishing a framework of accountability, while initially difficult, is possible due to the wide array of new incentive structures as well as new technology like wireless health devices and tele-services.

Rating systems, cost transparency, online social forums, and condition-specific social media communities now make it easier for current and future patients to reward providers who make the necessary care evolutions with their business—and avoid those who don’t, armed with the ability to publicly post their care experiences.

A patient outcome-focused approach will also require Healthcare to replicate the most influential detail of Amazon’s disruptive business model: bringing the desired outcome to the customer’s front door.

Tele-Care and Concierge Patient Care

Telehealth’s most exciting offering is the ability to deliver care when—and where—the patient needs it. Some telehealth companies combine wearable data collection with a convenient doctor-on-demand model. Soon, enhanced data collection from wearables, clinical informatics, and a patient experience-centered service framework will provide care in the convenience and privacy of one’s own living room—at cost savings for the patient and the health system.

While teleheath cannot replace an in-person visit in all circumstances, the ease of access and overall savings in transportation, time, difficulty, and paperwork cannot be dismissed. Various diagnosis can be accurately rendered through tele-visits, particularly when accompanied by data gathered by the EHR portal as well as other clinical reporting and analysis software that can do what a primary care provider traditionally cannot do: document and analyze everything relevant to patient care that occurs between visits.

Telehealth can reduce treatment redundancies, delays, readmissions, and improve care transitions. Preliminary trials are yielding positive results: telehealth increases and maintains the frequency and productivity of provider visits. Additionally, home-based care supported by wearable technology and monitoring services can save time, money, and increase personalization of care.

Interim Monitoring and Diagnosis

The software and infrastructure giving voice to Alexa and domicile-omnipresence to Nest can expand the reach of a care team, ensuring that patient populations are managed by exception by monitoring the ongoing results of each treatment in-between visits, virtual or otherwise. This information passed through a dedicated network, can be analyzed by “augmented intelligence”—software capable of providing analytical support.

Extended to evaluate a patient’s progress healing over time, or in pill boxes to monitor medication adherence, this technology provides new key insights into patient behavior. Remote glucometers can notify nurses, family, or other caretakers of dangerous hyperglycemic or hypoglycemic states and document the occurrence. In the future, they may be able to simultaneously order the necessary equipment and medicines required for treatment—based on the microsecond analysis of billions of data points, shared globally through centralized servers.

Additional software will ensure this information is securely stored and easily accessed by doctors and care teams for quick, documentation-free review.

Patient-Provider Relationship 2.0

The future of these technologies working in concert to improve patient experience converges at the point at which algorithms will transition to AI or Artificial Intelligence. When technology handles the collection, analysis, diagnosis, and reporting of a patient’s current condition, based on their individual behavior, genealogy, and health data cross-referenced with all known medical information in existence—what does that mean for physicians and their patients? Superhuman performance. This level of superhuman performance will allow physicians to get back to the business of being care givers and spend more time with their patients.

AI can exponentially enhance care team performance: cures, treatment, and prevention of diseases will be maximized when health professionals are able to devote their entire focus to patient care, rather than the maintenance of systems, documents, reports, information gathering, transitions, and all else required by our current healthcare model.

Properly incentivized providers -working with patients in partnership- can be expected to increase patient satisfaction while decreasing the amount of unnecessary procedures, tests, and misdiagnoses.

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A few years after going live on our enterprise Electronic Health Record (EHR), we decided to institute Data Governance (DG). We thought we understood the problem to be solved and the means to address it. We followed the recommended approach from our vendors and talked to a few like-minded healthcare institutions. We had a checklist and, though annoyed to an extent, we were going to put DG in place. Like installing a new light fixture, we approached this as a project with process-related deliverables. We had recently launched our enterprise data warehouse, and DG seemed like the reasonable thing to do. We focused on operational terminology and engaged a variety of stakeholders. Then, despite planning and preparation, we fizzled. Our participants didn’t fully understand what we were trying to accomplish nor how this DG work connected to our business value. Soon, we recognized that we didn’t fully understand what we needed to achieve or even what Data Governance is supposed to be.

At the same time, we were architecting a comprehensive analytics strategy to maximize data productivity and to prepare for Population Health. As a part of this overhaul, we elected to scrap our earlier v.1.0 efforts at Data Governance and take a fresh look at both the problem to be solved and the best framework for delivering solutions. We reached out to external industries, external health systems, and academia. We spent six months investigating. What evolved out of this fresh approach has led to an efficient and robust mechanism to ensure both data quality and usability of delivered information. Here, utilizing a Q+A format, I’d like to share a small portion of what we learned in our approach to DG and share a few of the accomplishments thus far.

Begin with the why. What problem are you trying to solve with Data Governance?

If the purpose of analytics is to deliver actionable insights into the operational effort, then without confidence in said data, it would be foolish to take action. First, it’s about the health of our patients. Agency for Healthcare Research and Quality (AHRQ) reported the results of a five-year study on Hospital Acquired Conditions (HAC) and noted that high-quality data was a driving force for improvements which included a 21 percent reduction in HACs, 3 million fewer adverse events, 125,000 lives saved and $28 billion in savings.

Closer to home, if our physicians and operational leadership don’t trust the data, then data generation is a futile exercise. When I put productivity or quality data in front of a doc, we need to know: (a) how confident are we in the quality of this data? and (b) how accurate is the data? Ultimately, it boils down to: what is the believability of the information that is presented? Although not a binary circumstance, the information quality must exceed an agreed upon threshold to motivate action.

We recognized early on that Data Governance could signify many things to many individuals

After considering the many variables at play, we ultimately decided on a mission statement, which captures the essence of our driving purpose:

To develop an organizational structure empowered with ensuring that our data assets are fit for use.

Data Governance can encompass many different aspects, how did you decide on where to focus?

We recognized early on that Data Governance could signify many things to many individuals. It’s easy to boil the ocean here. We were licking our wounds a bit from our DG 1.0 efforts and, by design, elected to focus our efforts on only four critical pillars that are directly applicable to a health system whose primary mission is patient care:

Accountability: We instituted four levels of responsibility from the C-Suite to Data Governors to Data Stewards to the Office of Data Governance. This includes both producers and consumers of data.

Proper Use: This refers to our ability to communicate accurately. When I say ‘Length of Stay” and you say “Length of Stay,” do we both mean the same thing?  When I read a report, which highlights “Delivering Physician,” do I know the definition of this term? Have we defined and agreed on key terms?

Quality: What level of confidence do we have in the information provided? Do we understand the data progeny? Have we profiled the data? Do we know the life cycle of the data? Has a data steward overseen the quality?

Movement: Data can move from our enterprise health system to other entities such as a payer database. It can also move from system to system within our health system (think patient satisfaction scores into our data warehouse). Do we understand how that data is collected, stored and moved? Are we sure that we have adhered to compliance and InfoSec requirements?

I haven’t heard you mention a specific Data Governance vendor or consultant. Who do you use for your DG platform and who have you consulted?

There are many competent consultants in this arena. However, we made a strategic choice to dedicate internal resources to developing our enterprise Data Governance effort, leveraging the skillset of our DG Manager and others. We recognized that we lacked specific competencies, but given our dedication to getting this right, we were convinced that we would expand our world and connect with entities which would help guide us. Ultimately, this proved to be correct as we’ve partnered with 13 other health systems throughout the country—each trying to solve the same puzzle.

Also, we built, de-novo, our data profiling tool, internal glossary, and data quality reporting mechanism. Eventually, we will look to purchase a DG Platform, but developing these products internally taught us great lessons on what we actually need versus what may be commercially marketed.

What are some insights that you have gleaned from this process thus far?

As our DG efforts are rooted in operational and clinical accountability, the work has been very public. This element alone has allowed executive leaders and physicians to peer into the world of DG in a fashion which provides confidence in the work we are doing. Recently, a respected physician colleague told me that “although no data is perfect, I’m confident that our system has the checks and balances in place to get it right.”

Seeing specific data quality indicators shows us to what degree we can trust the data.

Transparently seeing what data exists within our enterprise, we can leverage it to manage operations more efficiently.

Data Provenance allows us to see where the data originated.

Data lineage allows us to see how the data is transformed into systems within our enterprise.

We remain on our journey to a fully executed, enterprise Data Governance program within our integrated health system business model. At present, I consider our effort to be only part-way to this goal. Our 2.0 efforts have allowed us to establish a framework for scalability while assuring accountability within the operational leadership. Additional discussion regarding the change management elements of instituting Data Governance may be a worthwhile future topic.

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