It is no secret in healthcare today that electronic data capture leads to better policies, better programs, and better outcomes. This understanding paved the way for Electronic Medical Record (EMR) solutions which generate complete records of clinical patient encounters, supporting evidence-based treatments, strategic quality management initiatives, and clinical outcomes reporting. Each of these capabilities gives clinical and administrative healthcare staff actionable data for improving patient care. EMR solutions are equally important in remote and underserved areas as they are in wealthy states and nations, due to the fact that health disparities most notably affect subpopulations. According to a 2012 report from the Agency for Healthcare Research and Quality, “racial and ethnic minorities and poor people often face more barriers to care and receive poorer quality of care when they can get it.” EMRs can thus provide a benchmarking tool to drive quality improvements, and identify these trends to ensure that local and national authorities can improve care for subpopulations.
The troubles with EMR arise during implementation. Typically, EMRs can cost millions of dollars and take 12-18 months to implement, using time and scarce resources for development, training, and support. Furthermore, they often do not match the needs of the client hospital or organization. While many healthcare facilities have the luxury of allocating the time, money, and resources required for EMR implementations, these difficulties and demands present barriers to adoption for underserved and/or remote communities. Thus, EMR systems have been unable to solve the challenges impacting these facilities. Various nations, including the United States, have implemented legislation and allocated billions of dollars to expedite EMR adoption. However, the result has been that only the healthcare systems that can afford EMRs are the ones that benefit, rather than those in underserved and/or rural communities.
The warning signs foreshadowing a negative impact on the underserved in the U.S. were noted as early as 2010, when David Blumenthal, the National Coordinator for Health Information Technology at the U.S. Department of Health and Human Services, wrote an open letter asking health information technology vendors to help “in making sure that we are not creating a new form of ‘digital divide’.” In order to address the concerns around the digital divide, the government established laws that would deliver a greater reimbursement to Medicare/Medicaid providers in underserved areas. Yet a recent report from the Centers of Medicare and Medicaid Services shows funding gaps in rural, remote states. Larger healthcare organizations in states such as California received most of the funding for their Medicaid practices; close to $720 million dollars in Medicaid payments. New York’s vibrant healthcare eco-system received closed to $600 million. On the other hand, already struggling states failed to secure funding to provide equitable care. Arkansas received about $60 million, Iowa received about $100 million, and Nebraska only received roughly $40 million. Are we to believe that these rural states had less to gain from implementing EMRs and voluntarily left money on the table?
There is still a gap in establishing an electronic data capture environment for underserved communities. This persists because underserved communities do not have the ability or resources to get the assistance required. To understand this problem, it is important to identify the challenges associated with setting up these environments.
The implementation of EMRs to capture data in underserved areas has been fraught with problems, which have slowed their adoption. Among these problems are several key challenges we must examine, such as:
The lack of required infrastructure, such as networking and broadband.
The lack of capital for hardware purchases.
The lack of time, given the standard 12 – 18 months necessary to implement a solution.
The lack of both IT and clinical resources.
Patient medical history is typically owned by the provider and closed to other providers who may be treating the same patient.
The difficulty of using the software.
The misalignment of the EMR protocols with clinician workflow needs; e.g., designed to bill rather than treat patients.
These seven challenges highlight major roadblocks and barriers to EMR adoption in underserved communities and therefore inhibit our ability to structure healthcare-appropriate policies and review outcomes. How do we solve these challenges?
A New Model For Underserved Communities
Identifying these challenges and solving them are two different things. In 1998, my husband and I founded a company called Vecna, with the goal of providing patient-centric information technology solutions to the field of healthcare. Our work led us to realize that we could provide a robust, easy-to-implement EMR solution for underserved communities. We then established Vecna Cares, a nonprofit organization to provide technology and training to help strengthen health systems in underserved areas.
My journey of creating an EMR solution for underserved communities led me to rural clinics in Kenya where I discovered a model that can be applied to other developing countries and even to underserved communities in our own backyard. These lessons have informed the direction of a viable model and produced the beginnings of patient registries that can support good healthcare policies and outcomes. This new model is a lightweight EMR that was centered around ease-of-use and the fundamentals of data capture. It circumvented hardware and network obstacles of implementation, creating real value at each point of interaction. This model has taken root and, in Kenya alone, is serving a total estimated catchment area of 95,000 people with approximately 25,000 unique patient records to date.
The fundamental unit of this model is the Clinical Patient Administration Kit, or CliniPAK. CliniPAK is a mobile all-in-one system comprised of a tablet, power supply, local network, and customized software: an alternative to the issues typically associated with the lack of infrastructure required to implement an EMR.
This same model used in rural Kenya is just as useful for urban free clinic systems, such as those in Worcester, Massachusetts, which use the CliniPAK as a viable patient record system. In fact, the CliniPAK’s simplicity has become a practical asset. Worcester Free Clinic serves patients who are newly uninsured from job loss or young adults no longer covered by parental plans. Some are chronically uninsured, finding that they can’t afford the premium for healthcare plans, and a small number actually have access to health insurance but delays in getting appointments and high copayments lead them to call on the free clinics for pressing needs. Worcester Free Clinic had four locations, all with paper-based patient records. This model led to failed communications and disconnected longitudinal records when patients visited the multiple sites on different days. The Worcester Clinics adopted the CliniPAK to be able to record essential data about each patient visit and to retrieve that information upon subsequent encounters at the original clinic or one of the other sites. As a result, the Worcester Free Clinic is able to follow-up on patients, track health conditions, aggregate data to plan for future patient education and care programs, and report to local sponsoring organizations.
Another significant advancement CliniPAK presented for the underserved was its intelligent branching logic survey which could be configured by the end user without programming by IT resources. Instead of hard-coded templates that follow specialty and problem type found in most EMRs, CliniPAK offers simple, customizable surveys that guide the clinician through the documentation of patient care. The user interface is equivalent to completing a questionnaire rather than clicking in and out of screens with complex navigation. There are many benefits to this approach, including the elimination of extensive training and implementation timeframes. As a result, it takes only two weeks to implement the CliniPAK; one week to train on the system, and one more week of training for generating data reports. This stands in stark contrast to traditional EMRs that need 12-18 months for implementation and training. We have found the same to be true in our international work, such as at a clinic in rural Enoosaen, Kenya, where I delivered a CliniPAK to the clinician and she immediately started using the program without any training. From location to location, the same pattern emerges. For the first few days, clinicians ask questions frequently. By the end of the week the clinicians are almost completely proficient with the program. The same is true for generating reports. The first few days of training are filled with questions, but by the end, the clinicians are self-sufficient.
I also came to understand, however, that remaining stagnant in clinics is not enough. The stagnation does nothing to combat the problem of limited aggregate data, which can impede a clinician from effectively learning about and treating their population. It became important to build a network of CliniPAKs that can connect between different communities and aggregate data. This allows the information to go into a local database, which could then be shared all around the area. Networking enables peer-to-peer connections, dynamic data viewing for trend spotting and potential outbreaks, and resource monitoring. With this data aggregation, clinicians can achieve better outcomes.
According to the World Health Organization, 23 previously polio-free countries, including Kenya, were re-infected between 2009 and 2010. To prevent a widespread epidemic in the CliniPAK project’s target Transmara district of Kenya, health workers were deployed for a massive immunization campaign of 1.8 million children under the age of five. As a result of incomplete records and information gaps, already limited health worker resources left the clinics for months to seek and identify children in the community who had not been immunized against polio. This time-consuming process of knocking on doors was not only inefficient, but also negatively impacted the capacity of the clinic base and left substantial opportunity for human error.
To underscore this problem, baseline data gathered by the CliniPAK in the same district shows great discrepancies in the number of live births recorded versus the number of children immunized. The CliniPAK recorded only 1,856 in-clinic live births in 2011. However, CliniPAK also recorded 7,455 newborns that received tuberculosis immunizations. Disconcertingly, only 3,551 received the first polio vaccine during that same time period. The discrepancy between the number of births recorded and the number of vaccines received represents the number of home-births that go unrecorded. By extension, there is no way to estimate how many children with unrecorded births go through their first year without a visit to the local clinic, and thus go unvaccinated. These children become a risk to public health.
Another problem is the current paradigm for administering immunizations and providing care. In this paradigm, clinicians are reactionary and wait at the clinic for visitors. However, as evidenced by the discrepancies in the live birth rates versus children immunized, not all parents visit the clinic with their children. This leaves the community vulnerable to outbreaks and disease like the recent polio epidemic. To create a convenient venue for capturing defaulter information, clinicians and health workers must make proactive efforts to visit the community in the form of house calls or visits to centers of activity and ensure accurate records for these visits. With the CliniPAK, clinicians can track their patients, and are alerted when patients should come in for a visit or are due for a vaccination.
Through the CliniPAK network, Vecna Cares has learned that we can create patient registries for aggregating important trends and streamline the reporting process to the government. This work was previously done in the clinics on ledger-size paper registers, which were hard to navigate. Then at the end of each month, clinicians generated the reports by hand and closed the clinic for at least three days in order to travel to regional government facilities to deliver the reports. Using the CliniPAK, clinicians can produce reports electronically, which saves time and allows the clinics to be open for patient care for three more days a month. Information is transmitted off site to a centrally hosted server and is compatible with any preexisting EMR system.
As an example of how this works, the simple charts below display the top values of data collected across disease programs in Kenya after CliniPAK was implemented. This data is the result of 10,691 patients recorded at eight clinics. This depth and assurance of data had never before been available, allowing district health teams in Kenya to make a data-driven evaluation of health burdens, interventions, and allocation of resources.
Vecna Cares has found that the CliniPAK units and CliniPAK networks are supporting data capture needs for public and private health ventures such as pop-up clinics, U.S.-based free health clinics, and medical missions. This portable IT platform and streamlined user-interface enables clinicians in underserved or developing areas to quickly establish point-of-care patient data capture at low cost, using available and local resources, including doctors, nurses, staff and even volunteers.
The model established by the CliniPAK proves that effective data capture can provide insight into better health outcomes and better policies. Clinicians can now understand the health and ailments of their population, monitor resources, and receive data-driven evaluation of health burdens.
People always ask why Vecna Cares decided to build a technology infrastructure in Africa when there are so many other basic health necessities that must be met – like access to doctors, treatment, and medications. The answer is that information technology creates a way to see and understand where doctors, treatments, and medications are most needed. With this information, the disenfranchised can understand and communicate their biggest challenges in advocacy for themselves and the people they serve for more accurate, holistic patient care and better outcomes for all.