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The Age of the Electronic Medical Record

The rapid adoption of electronic medical records (EMRs) was catalyzed by government mandate and financial incentives to encourage healthcare practitioners, clinics, hospitals and systems to effectively transition the documentation within the electronic medical record from paper-based, manual processes to electronic and digital platforms. The rise of several electronic health record software companies eventually gave way to a handful of significant players who maintained dominance of the market for several years. While the survival of Epic, Cerner, Allscripts, Meditech and athenahealth promotes healthy competition, the implications on interoperability and data sharing are profoundly impactful. Today, the tight control each software vendor has maintained with their clients has limited the ability to share data in a meaningful way to find solutions to complex population health problems or gather relevant case studies for rare diseases. However, this tight control and corporate competition likely helped drive the adoption and utility of electronic medical records among the front-line users such as physicians and nurses. Despite the view that EMRs are costly, burdensome to physicians and interfere with the doctor-patient relationship, wide spread adoption is continuing.Footnote 1 Epic Corporation was founded by the daughter of a physician who ultimately recognized the gross inefficiencies of hand-written medical charts and the inadequate use of historical charts to help patients.Footnote 2 As more and more entrepreneurs joined the race to create the ultimate electronic medical record, the adoption of the digital age in healthcare had begun.

Physicians who enjoyed creating detailed medical records for each patient encounter were a rare breed in comparison to those physicians who managed to capture the bare minimum amount of information in illegible notes. With the added pressure of productivity metrics such as RVU compensation or maximizing surgical caseloads, the volume of patient data was exceeding the ability of any individual to review and interpret on a regular basis. The added challenge was the variety of formats, hand-writing, abbreviations and clinical jargon that existed between different practice groups. Depending on your medical training in residency, fellowship and even medical school, the expectations for clinical documentation were not consistent across geographies. The concept of typing clinical notes or using a computer word processing software to capture this information was generally regarded as a distant dream that might happen in several decades. Leading healthcare organizations made initial investments to create their own proprietary medical record system or enlisted the help of these digital health entrepreneurs who offered a solution that could be used out of the box. Organizations like Kaiser Permanente chose a product from EpicFootnote 3 while other systems like Geisinger chose an alternate product from Cerner Corporation for population health.Footnote 4 During this time, there was no clear mandate to use electronic health records or penalties by way of government reimbursement to encourage wide adoption. The first movers who boldly took the risk by investing in the software soon faced the challenging task of achieving widespread stakeholder adoption and engagement.

Physicians are trained from an early age to excel at acquiring large volumes of scientific data and applying this information in a systematic way to help cure disease or alleviate suffering. The Hippocratic oath is usually taken at the end of medical school before a physician or surgeon embarks on another rigorous path of learning through an apprenticeship model. The focus on core sciences leaves physicians with very little bandwidth to explore other academic pursuits, such as music, literature or computer programming. Fast forward to the early years of electronic medical records and you have a population of extremely intelligent over achievers all able to perform procedures or deliver a differential diagnosis, but with limited computer or word processing skills. The newly introduced expectation of using computers during the practice of medicine was no doubt a challenging experience for many physicians young and old. Like any attempts at changing human behavior, there was a spectrum of responses ranging from angry rejection to joyful acceptance. Rebellion, outrage, burnout, cynicism and other emotional extremes could have impeded the progress of the software implementations, but it is more likely that physicians responded with cautious or reluctant acceptance. To those who viewed the problems facing the healthcare industry with an astute perspective, the transition to an electronic health record was inevitable, so why fight the change? The proposed benefits of more efficient workflows, greater patient satisfaction and access to information previously unavailable convinced many physicians to take on the challenge and struggle through a clunky implementation project at the mercy of the IT department. The results were mixed as some organizations reported immediate benefits from using the software while others struggled to regain productivity, profitability and physician buy-in.

The surge in electronic medical record implementations followed closely behind the introduction of the HITECH Act and Meaningful Use.Footnote 5 For the first time, the U.S. government was in support of widespread adoption of some form of electronic medical records and used a combination of legislation, policy, incentive payments and reimbursement penalties to accelerate adoption of a software solution for paper and hand-written medical records. The specific reasons why physicians started to finally embrace the effort to move away from paper-based records could have been one of many, but it is safe to assume that financial incentives coupled with financial penalties for non-compliance were strong motivators for behavioral change. If clinical providers were salaried employees of larger health systems, the decision to adopt an electronic medical record was usually made without any of their input or agreement. This perceived oversight or lack of collaboration served as the basis for many physicians and clinicians from fully engaging in the adoption and integration process. There are several well documented examples of electronic medical record implementation failures across the U.S. In one example, the medical group affiliated with a large hospital in California felt neglected when they were not included in the decision to purchase a specific software product. When it came time for the IT implementation, the physician group remained detached. By the time the system went live, the physicians reacted by refusing to use the system and instead reverted to manual paper-based documentation processes while providing care. The hospital leadership eventually succumbed to the demands of the medical group and had to convert the current electronic medical record to another vendor solution that the physicians preferred. There have been many organizations that have also switched between software vendors such as Epic and Cerner due to early struggles following implementation impacting operations or financial performance or because of merger and acquisition activity.Footnote 6 The notion that consolidation and standardization leads to cost savings and greater efficiency is carried over from the success of large health systems such as Kaiser, Intermountain and Geisinger who used similar approaches to manage hospital and ambulatory operations. Independent physicians and free-standing community hospitals tended to delay spending to implement a new electronic health record and preferred to watch and learn as others went down the path towards the EMR first. Even then, sometimes the capital requirements to meet the demands of Meaningful Use or other legislation forced physicians or smaller hospitals to seek assistance in the form of an acquisition partner who would then invest to implement the needed technology.

The key lessons learned from observing the gradual adoption of electronic medical records over the past decade are as follows:

  1. 1.

    Adoption takes time—the complexity of healthcare systems and the personal nature of medicine make it unlikely that drastic changes will spread quickly and decisively

  2. 2.

    Healthcare stakeholders need incentives to drive change—Meaningful Use incentives and penalties created an irresistible pull for many organizations who viewed financial success as a critical part of their mission

  3. 3.

    There isn’t a single magic bullet solution—the variation across software solutions and no single dominant player indicates that different organizations and patient populations require customized or localized solutions to meet their needs

  4. 4.

    Alignment from the executive office to the front lines will accelerate overall engagement but doesn’t necessarily guarantee rapid adoption—stakeholder alignment is a pre-requisite for project success, but implementation plans still need to be systematic and dedicate enough time and resources to key components of the process such as change management and training

The Age of Big Data

The steady but persistent adoption of electronic medical records (EMRs) created growing databases of structured and unstructured clinical, financial and operational data. The promise of data-driven insights derived from the volume of collected information was one of the reasons EMR adoption gained momentum. Research studies benefitted from the easily accessible and categorized clinical charts compared to the previous experience of trying to collect and coordinate huge piles of paper charts with incomplete information in many cases. Revenue cycle departments gained access to more accurate and complete patient encounter records and clinical documentation to align with claims submissions and medical necessity reviews. The rising number of clinics, physicians and hospitals adopting EMR systems contributed to the data explosion that many organizations were not prepared to take advantage of. Those that did were able to apply business intelligence to the data and create clinical decision support tools, revenue cycle integrity practices and patient experience metrics as examples of successful implementation of analytics.

Having a repository of discrete data captured in the EMR gave physicians and other users the confidence they needed to accept the insights derived from any algorithms or analytics applied to the data sets. Reliability and reproducibility of data is a key factor in the eventual adoption and successful implementation of any dashboards or performance metrics used to support change. The use of evidence-based protocols and primary research sources have traditionally been used to convince stakeholders that a more proven methodology or process can be used instead of the current state. Even more powerful is the dissemination of peer-reviewed literature produced by authors that maintain some relationship with their colleagues in a selected sub-specialty or discipline in healthcare. Once the data has been blessed, it makes it easier to scale solutions to impact a larger number of stakeholders. The next hurdle to overcome is the wide range of applications that can be leveraged to manipulate data and to find the right solution for the problem at hand.

Scaling a concept to impact the greatest number of stakeholders is the dream of many entrepreneurs who have overcome adversity to achieve eventual success. Historically, the path to achieving this goal was well understood within the healthcare industry. New entrants into the healthcare space slowly developed their product or solution and gradually gained enough visibility to capture sufficient market share. The rise of new digital health companies continues to help push the envelop as to what is feasible for conservative, budget-conscious executives. However, many of the most promising start-up companies are facing cultural and logistical challenges that consume their time and resources. One approach to do is bring talented, like-minded high performing individuals to serve as champions for the adoption or change management process. A digital health startup may have the potential to solve very challenging and complex problems, but without advocates and champions across the various layers in a hospital or healthcare setting, the barriers to success are discouraging. A foundation of quality data is almost a prerequisite since many stakeholders evaluate novel ideas through objective measures and apply the same scrutiny previously reserved for research articles or journal publications. Merging reliable and reproducible data with strong champions across the organization has shown to accelerate the spread of entrepreneurial endeavors.

Big data by itself is not enough to win over all the relevant stakeholders to drive implementation of new ideas. The real value of the data comes from the insights or predictive models that can be derived from the aggregate information. It is important that gradual education and sharing of new ideas take place before any radical changes are introduced. Sometimes the culture and supporting infrastructure are not in a mature enough state to maintain the growth and development of new ideas. A carefully thought-out approach combined with effective execution of the strategic plan that includes big data as a component will likely be better positioned for success than forcing stakeholders to accept a new workflow without their early buy-in. The big ideas or “moonshots” tend to generate a lot of publicity, but it is the smaller, less glamorous projects that focus on solving relevant and practical problems that can generate positive early results when successful.Footnote 7 Learning from the challenges of adopting big data for practical applications in healthcare provides another example of how to slowly disseminate a fundamental change in behavior and workflows through the introduction of a new decision-making tool.

The key lessons learned from the rise of data repositories because of wide-spread electronic medical record implementation and usage over the past decade are as follow:

  1. 1.

    You can’t engage downstream stakeholders and users without high quality, robust and accurate data to build credibility and eliminate one of the most common reasons for poor adoption and failed implementation of data tools

  2. 2.

    After establishing the data source is reliable and relative free of significant errors, the continued use of analytic tools is determined in large part by the driving force between the key performance indicators (KPIs). Be cautious of KPIs focused too heavily on financial or technical goals over clinical or quality ones.

  3. 3.

    Regular review and realignment of organizational goals and outside trends is needed to keep the performance targets of the data analytics consistent with the strategic objectives year after year.

  4. 4.

    The ability to scale and handle the exponential increase in data volume requires significant computing power and storage capabilities. A cloud migration strategy to integrate the data warehouse and the software applications needs to be carefully executed to avoid significant performance issues that could erode confidence in the data itself.

The Age of Value-Based Care

The increasing costs of delivering healthcare in the United States prompted the previous administration to enact several pieces of legislation that mandated the slow but inevitable migration of care delivery from fee for service to value-based care (VBC) models. Although the recent change in party leadership has threatened to undo several key features of the Affordable Care Act (ACA), otherwise referred to as Obamacare, the bipartisan support of value-based care initiatives reflects the stark reality that without significant intervention, the cost of healthcare in this country will outpace any attempts by politicians to control it.Footnote 8 The challenge lies in the incentives currently offered to healthcare organizations and physicians to generate revenue sometimes at the expense of the tax payers and the administrative expenses generated by health insurance companies and other non-essential parties that feed off the wasted dollars consumed and show no impact on health or outcomes. Value-based care is a noble aim and despite enormous effort and almost universal acknowledgement of the unsustainable course the healthcare system is on, the adoption of new policies, standards of care and well-intended technology have barely begun to make any change to the cost structure of the U.S. population.

Measuring the true impact of new healthcare policy at either the federal or state level down to the individual patient in a rural town requires the appropriate definition of what is the desired goal. There is no shortage of opinions around what the most important attributes are in our health system. Are we trying to extend the average life expectancy for all U.S. males and females? Do we want to lower the average per capita cost of delivering care? Is the elimination of certain chronic diseases or cancers an indication of how superior our healthcare system is compared to the rest of the world? A recent research article released by the World Health Organization (WHO) placed the United States at number 37 for overall health system performance. The Organization for Economic Cooperation and Development (OECD) in 2017 pointed out that the United States spends the most of any developed nation on healthcare but does not achieve better health outcomes for life expectancy at birth, infant mortality, management of asthma or diabetes or heart attack mortality.Footnote 9 How is this discrepancy explained between the amount of resources spent on healthcare in the U.S. (According to CMS, in 2016 17.9% of GDP was spent on healthcare which equals $3.3 trillion or $10,348 per person)Footnote 10 compared to measurements of performance? There is simply no easy answer but the move towards value-based care is an attempt to stop the bleeding before costs create a national budget crisis.

The single largest insurer in this country is expected to run out of funds needed to maintain Medicare, Medicaid and a whole host of other healthcare programs that millions of Americans depend on. A recent report released in June 2018 from key government program trustees revealed that Medicare will run out of money 3 years sooner than expected in 2026.Footnote 11 With this knowledge and the prospect of a failed system to care for the country’s most vulnerable, there has been modest engagement across all levels of healthcare leadership to bend the cost curve and prolong the life of Medicare and other similar programs. While it is unlikely that solo or group practitioners will dramatically alter their current way of practicing medicine to save Medicare, larger organizations like Kaiser Permanente have strong leadership in place to implement value-based care programs that can impact the population on a grander scale. The recent increase in merger and acquisition activity across healthcare has folded many physician practices into health systems which move quickly to integrate new partners. Some view this activity as precautionary to prevent increased competition in a time of declining margins and reimbursements. Financial pressure on federal, state and local governments also put strain on the private non-profit health systems who care for a large percentage of the Medicare and Medicaid populations. The outcome of this stress produces long-lasting changes to workflows designed to lower the cost of caring for patient populations who do not generate profitable reimbursement.

Successful organizations have been able to adopt value-based care initiatives through internal projects or by bringing in outside expertise and leveraging recent wins. If there is no previous momentum around making the transition away from a fee-for-service model, the journey can be a long and arduous one. Early adopters of value-based care discovered that it was a difficult task to suddenly ask healthcare stakeholders to change the way they had been practicing medicine for decades if not generations. Physician champions or leaders were put in the middle of tense conversations between executives and clinicians. Even though the reasoning behind value-based care made sense, the reality was that financial contracts and incentives did not reward a more holistic approach to delivering cost-effective and outcomes-based care. Furthermore, some clinical departments lacked the project management experience to drive systematic process improvement with governance and change management. The result of these circumstances led to very slow incremental changes that did not significantly bend the cost curve or cause widespread behavioral change across clinical areas. Even today, many organizations still compensate physicians based on volume or RVUs and maximize financial returns without much thought given to better outcomes and lowering the cost of care burden. The success of some health systems to make significant progress in achieving the objectives of value-based care demonstrate that there is not a single uniform path to reach this goal. Rather, it is a pain-staking, complex journey that requires engagement and support from all areas of healthcare sharing the same goal of fixing a broken system for the benefit of the patients.

Value-based care resulted in a gradual movement that is still in the process of transforming the healthcare industry today. The focus on uncontrolled and unsustainable rising costs coupled with the misaligned incentives for hospital, doctors and executives led to legislative attempts to course-correct one of the most expensive and personal industries in the country. Some of the key drivers behind the expansion of value-based care include:

  1. 1.

    Financial and budget constraints at the level of federal, state and local government

  2. 2.

    Poor performance of the U.S. healthcare system when compared to the rest of the developed world and adjusting for average GDP expenditure per person

  3. 3.

    Shift towards quality performance and outcomes-based incentives for providers and payers

  4. 4.

    Consumerism in healthcare with changing population demographics and consumption patterns

The Age of Digital Health

The proliferation of digital health companies, applications and devices in healthcare has changed the way we think about innovation in medicine. The exponential growth in high speed internet service and wireless fidelity (Wi-Fi) access along with the ubiquitous nature of smart devices such as the iPhone, iPad and Apple Watch has created the foundation necessary for digital solutions to impact industries and business processes.Footnote 12 Some of the most profound examples of how a digital technology company has completed transformed the industry it evolved within include ride-sharing companies like Uber and Lyft, accommodation rental platforms like Airbnb, media and entertainment offerings like Netflix and Hulu and food delivery services like Postmates and Grubhub. The unifying theme of all these digital technology titans is the dramatic way they have transformed how normal business is conducted and the new standard that customers expect, while decimating competition that failed to adapt to the new norms of operating in a digital age. Healthcare has remained more resistant to dramatic industry disruption thanks in large part to the layers of regulation, compliance and regard for human safety. However, the demand is growing for digital health solutions and companies disrupting normal operating workflows to meet the consumer demand of growing populations of patients such as millennials and future generations of savvy buyers. The ideas of convenience, crowd-sourcing and virtual care have already created niche industries where patients can receive a telehealth consultation, order prescriptions and pay for services without leaving the comfort of their own home or other popular destinations. However, there are still significant challenges for these companies to enter the mainstream of healthcare delivery and convince established leaders to adopt digital health and accept the risks with any innovation. Digital health faces challenges to achieve widespread adoption and practical integration into the current healthcare landscape and infrastructure.

Healthcare providers are seeing a widening generational gap between themselves and their patients. A large segment of physicians, nurses and executives are considered traditionalists, baby-boomers or generation X. As the population ages, people born in generation Y, generation Z and the millennials are finding themselves in need of various healthcare services. Consumer behavior and expectations have shifted dramatically and in a short time coinciding with the proliferation of smart devices, internet access and technology applications. Instant communication and convenience are prevalent in multiple industries such as retail shopping, banking, dining and leisure travel. The ability to order food, make reservations, pay bills and communicate via text or emojis from a mobile device is transforming how companies engage their customers. Although healthcare is more than just a collection of simple transactions of goods and services, the growing sentiment among generation Z and millennials is to make healthcare as convenient and accessible as other necessities in life.Footnote 13 This dichotomy that exists between providers and patients has contributed to the slower adoption of digital health solutions and for many new entrants into the industry, caused their eventual demise. The demand for digital health solutions continues to grow, but the current supply of validated, compliant and evidence-based applications is limited and not enough to meet expectations. The result is a misalignment of priorities and a lack of empathy for each group’s point of view. The acceptance of change and adoption of new care delivery models beginning with selected medical specialties or patient populations is starting to penetrate years of complacency and the reluctance to break from the traditional practice of medicine.

A major obstacle for digital health adoption is the ability of front-line staff and providers to become efficient users of a new technology or application. This challenge mirrors the difficulties faced by electronic medical record companies as they attempted to train thousands of providers to put down the pen and pad while turning to a computer keyboard regardless of their word processing or typing abilities. Frustration can be a long-term symptom of poorly integrated digital health solutions if the proper training, change management and elbow support is not in place. This frustration can easily turn to rejection of the solution or technology despite the positive benefits it may be able to demonstrate with continued usage. Careful planning and strategic mapping of key activities and milestones to engage stakeholders early is one approach to avoid poor adoption. Realistic expectations around how much training can be deployed and absorbed in relation to the group’s baseline technical abilities can reduce friction when productivity and workflows do not return to baseline as quickly as planned. Applications can’t be bolted on to existing tools without ensuring that workflows will be maintained and integration is achievable in a reasonable time frame. A one size fits all approach does not apply when you have a diverse and sophisticated work force that is accustomed to functioning at a high level at all times and understands the sensitivity of change when a patient’s health is potentially at stake. Achieving the desired level of competency for a digital health tool requires a thoughtful and well-executed strategic plan that addresses the unique needs of the core users and bolsters their confidence with steady progression to a desired proficiency.

Another determining factor for digital health dissemination is the credibility and reproducibility of the underlying programming and data characteristics. During the rise of big data and analytics, physicians were quick to discredit algorithms or analyses that they did not fully understand or have visibility around the details. Some stakeholders can feel threatened when a new technology offers insights that seem to be generated from a non-medical or non-scientific formula. Despite the rigorous demands of computer engineering and data science programs that serve as the foundation for digital health solutions, medical professionals are slow to accept that a new idea originating from outside the industry can improve the current standard of care. A collaboration between clinicians and engineers or programmers in the form of a digital hackathon can create synergy and a deeper appreciation for each discipline. Transparency and sharing knowledge assist to drive support for digital health in organizations where multi-disciplinary teams work together to solve complex problems. This culture tends to be more receptive to outside contributors and can readily implement new technologies that have already been considered or discussed internally. When health organizations review data consistently and apply analytical tools to help mine for insights, it fosters an environment that values evidence-based approaches to clinical problems. This may result in higher standards for achieving recognition but is valuable to help identify quality initiatives that are sustainable and grounded in fundamental objective data to drive physician adoption.

Digital health implementation efforts also need to consider governance structure, data protection and cybersecurity along with current value-based care requirements. The volume of innovation and technology solutions can be overwhelming, and many organizations rely on the leadership of a Chief Information, Chief Innovation or Chief Intelligence Officer to help evaluate multiple options. Not all organizations have identified this leadership role and instead depend on seasoned executives who may not have the requisite background to fully evaluate the feasibility and applicability of new emerging technologies. The recent string of healthcare cybersecurity incidents has resulted in the loss of millions of personal health records containing sensitive information and increased scrutiny by organizations to identify their own vulnerabilities. New threats can distract leadership from considering substantial investments in unproven areas and instead increase their ongoing budgets for data security measures or infrastructure upgrades. Taking a conservative approach and being fiscally responsible is a comfortable approach for veteran hospital leadership, but this cultural preference makes it challenging for innovative digital health opportunities to gain traction and broad support. When an organization has achieved a robust data security infrastructure and has a forward-thinking governance and leadership in place, advancing projects in digital health is more achievable.

The challenges facing entrepreneurs in the digital health space can be daunting and may stifle creative ideas that require perseverance and patience to succeed in the healthcare industry. History suggests that the emergence of innovation in healthcare takes several years to reach a significant level of dissemination and adoption. The gradual implementation of electronic medical records was incentivized by government programs like the original Meaningful Use and HITECH Act that motivated physicians and healthcare organizations to invest in technology and change workflows. The rise of big data and analytics depended on high quality and reproducible data sets that withstood the scrutiny of skeptical physicians and other end users of the information. Slowly, stakeholders became comfortable with the tools and objectives of big data and started to see the benefits of continued adoption of analytics. The eventual realization of healthcare leaders that a volume driven or fee-for-service industry is unsustainable led to the introduction of more regulation by government to curb costs and shift to value-based care. The implementation of various quality reporting programs provided a combination of incentive payments or penalty avoidance along with the expected improvement to health outcomes. The overarching theme behind general adoption of new processes or solutions in healthcare is the alignment of not only incentives, but also the identification of what is most important to various stakeholders. Motivating people to change certain behaviors that pertain to an individual’s health or personal values is a complicated and often time-consuming process. The momentum behind digital technologies across other industries may proceed at a break-neck speed, but in healthcare we are seeing a gradual adoption with pockets of hyper-activity depending on the specific demand or availability of a digital health innovation.Footnote 14

Summary

The future of digital health is going to introduce even greater change to the healthcare industry in the form of artificial intelligence, blockchain, wearable devices, virtual care and other technologies that will be applied to medicine in unique ways. One of the greatest barriers to adoption and knowledge sharing is the resistance of patients, providers and administrators to the unknown and untested. Scientific evidence has long been the gold standard against which new research and medical therapy is evaluated. However, the application of artificial intelligence in the form of computational decision making and cognitive learning using deep neural networks can greatly accelerate the time to bring novel ideas and therapies to the forefront. The expectations and needs of each generation has shifted towards a more on-demand and convenience focused life-style where it is normal to have access to almost all aspects of a person’s preferences through a smart device connected to the internet. Healthcare is facing the challenge of adapting to the needs of a younger patient population and an aging workforce that bring differing views on how to best deliver effective, compassionate and cost-effective care through current technology.