As we’ve been becoming more patient centric in healthcare, we have become more and more aware of the challenges different demographics face while trying to receive the care they need. As a result, health equity has been a hot topic and organizations have been searching for solutions. Because in order to put our patients first, we need to be sure that includes all of our patients, not just some of them. But just as important as finding solutions are, we need to be sure that we do it right.
A problem as deep as inequality can’t be fixed with temporary solutions just like you can’t fix a leaky pipe with some tape. You need to dig down and find the root of the problem and start the fix there. In healthcare, the place to start is with data. All of the decisions made in healthcare are based on the data we gather, so we need to make sure it’s sound. Are we getting the representation needed to cover all of our patients? Are there biases in how the data is gathered or interpreted?
We reached out to our amazing Healthcare IT Today Community to get their insights on the importance of data in achieving health equity. The following is what they had to say on the subject.
Deepti Sharma, Senior Vice President of Product Management at HSBlox
Improving Health Equity requires proper measurement and interpretation of available SDoH data. It is a complex problem due to the variety of structured/semi-structured/unstructured data sets that resides in multiple source systems, the lack of standardization in data collection and processing, as well as the need to capture large number of demographic, environmental, and socioeconomic metrics not yet measured today. Health equity outcomes can be improved only by having access to good data in order to make better decisions. This includes not only the clinical data for the patients, but also their social data that impacts their life and well-being.
Before we can apply statistical models to the data sets in order to develop composite risk scores for patients for Health Equity, we need to look at the available external and internal data sets (i.e., from both public and private sources) and the formats in which they are available, then co-relate points between the data sets to ascertain the entities, attributes, risk category domains, and measures across which analysis needs to be done. This can be achieved using a data engineering framework that is flexible enough to handle structured, unstructured and semi-structured data-sets to create a Patient LHR (Longitudinal Healthcare Record) to get a 360-degree view of the patient’s health.
Dr. Ben Barlow, Chief Medical Officer at Experity
Providers can utilize EHR documentation provided by social workers, community health workers, case managers, or nurses – all who are members of the community hearing firsthand from patients the health disparities they face. Capturing and using this data can drastically impact care delivery because social determinants of health (SDoH) influence health outcomes in very direct ways, which makes interoperability and capturing this kind of patient data so important. SDoH billing codes included in EHRs allow providers to paint the full picture of the external influences on a patient’s condition and enable more tailored patient treatments. If a patient’s SDoH status is incorporated in their care plan, these status codes can better show the complexity of the overall patient population being served as well – improving health equity and fostering more informed conversations and treatment across the continuum of care.
Andy Flanagan, CEO at Iris Telehealth
Healthcare IT companies can make an outsized impact on serving health equity and social determinants of health challenges. Our responsibility is to address the data bias that exists pervasively in our algorithms. Health equity and social determinants of health are defined by the conditions of an environment where people are born, live, and work. Depending on an individual’s circumstances, these conditions can create and exacerbate mental illness and make it difficult to access mental health care. Despite these challenges, healthcare organizations have the power to make meaningful change by bridging the gap between underserved populations and high-quality mental health care. By investing in solutions like telepsychiatry, healthcare organizations can positively impact their communities’ mental health.
Steve Lefar, Chief Strategy Officer at Strata Decision Technology
Integrating health equity data analysis into cost effectiveness and margin analysis reveals that investments in health equity are healthy for the patient and the bottom line. Many organizations are realizing that health equity investments align with their healing mission and strategic goals, and address critical issues such as access, lack of coverage, poverty, substance abuse, health education, and chronic care. Neglecting these issues can drive poor outcomes and overuse of emergency services, resulting in negative margins. By incorporating health promotion and equity into finance and margin analyses, organizations can see the positive impact on both patients and their financial performance.
Timi Leslie, President at BluePath Health
As part of the work we do at Connecting for Better Health, a large stakeholder coalition to advance data sharing across California, we know that data is power. Effective health equity work relies on access to high quality data to break down silos at scale and reach across counties and public, private, and nonprofit organizations in healthcare and social services. Data is the connective tissue that allows us to go further and faster together.
Shruti Kothari, Director of Industry Initiatives at Blue Shield of California
Health equity starts with equitable access to data. Only through the robust and secure exchange of health and social information can we most comprehensively understand social determinants of health and effectively respond to resulting complex needs. Data is foundational to a whole person approach to care, supported by value-based payment models and high quality care coordination.
Paulo Pinho, M.D., Vice President and Medical Director of Innovation at Availity
High-quality real-world data is primed to be one of the most impactful tools for driving preventive health, improving health outcomes, and promoting health equity. This is especially true in at-risk communities most impacted by social determinants of health (SDOH), where data quality is often hindered by numerous factors, including but not limited to lack of resources and time for physicians to address health issues, fragmented primary and specialty care, inconsistent health maintenance, limited screening for health-related social needs and overall difficulties with data collection. To level the healthcare delivery playing field, addressing data quality gaps is a must. A semantically normalized record that harmonizes multi-source and multi-format data from all care episodes that conforms to national data quality standards can help eliminate some of the data quality challenges enabling healthcare delivery to respond to patients more effectively and equitably – resulting in better care for all.
Bronwyn Spira, Founder & CEO at Force Therapeutics
First and foremost, all healthcare stakeholders must take concerted, intentional action to examine the disparities present in their own communities and patient populations. This requires a focus on data-driven decision-making surrounding how patient access, outcomes, and experiences may vary across social determinants of health. We will likely see reimbursements being tied to the demonstration of equitable outcomes and equitable provision of services across patient populations and geographical boundaries. Equally important is taking action on these evidence-based insights by implementing processes and tools to reduce barriers to care, even if this means having to fundamentally rethink how care is currently being provided. The growing adoption of digital healthcare tools represents an important step forward; for example, implementing high-quality remote care management, education, monitoring, and communication can help patients save time, co-pays, and lost wages while enabling a larger group of patients to access the care they need.
Carolkim Huynh, Director of Clinical Outcomes at Shields Health Solutions
In specialty patient care, clinical pharmacists are uniquely positioned to help identify and address social determinants of health (SDoH). They are often the most accessible and trusted members of a patient’s care team. By utilizing a patient management system, healthcare providers can leverage data insights effectively. To help address SDoH barriers, it’s essential to combine high-touch human connection and engagement with data and technology.
Amit Phadnis, Chief Innovation & Technology Officer at RapidAI
While there has been increasingly more attention paid to the problem of bias in AI, more action must be taken to address it and to develop ways to quantify possible bias in the data sets in order to create truly equitable AI. Not only do we need more diverse datasets to develop AI algorithms, but we also need improved processes for gathering information on how AI is working for different groups of patients in practice and for Incorporating data on social determinants of health. We know that there are a variety of social and environmental factors that influence a person’s health, and it is necessary that these factors be accounted for so that AI can more accurately predict or assess a patient’s condition. Continued measurement, analysis and sharing of patient outcome data across our systems is vital for fueling innovation and change focused on access to and equity of care. The technology is there; how we collect and apply the data is still progressing.
Rosemary Kennedy, Chief Health Informatics Officer at Connect America
By using personal emergency response systems (PERS) and AI-enabled remote patient monitoring (RPM), healthcare organizations can facilitate the collection of data related to social determinants of health (SDoH), including access to food, medications, or transportation, to help more effectively manage their care. With this critical data, they can better identify nonmedical challenges, such as feelings of social isolation, and connect individuals to the appropriate services that can help address many of the factors that disproportionately affect aging and at-risk populations. Additionally, these technologies can offer multi-lingual support and can also be used to identify trends or better understand risks within patient populations.
Mike Munsell, Director of Research at Panalgo
To achieve health equity, we first have to address the lack of diversity in clinical research and data collection. Historically, women and communities of color have not been adequately represented in clinical trials. In order to fairly recruit a representative set of participants and improve clinical trial design, it’s critical that organizations conduct analyses that are generalizable across a diverse set of patient characteristics at the planning phase. Data from real-world sources, for example, are more likely to incorporate this type of patient representativeness and heterogeneity. More diverse and equitable clinical trials will ultimately lead to improved health outcomes for all populations, regardless of race, sex, or other factors.
Jean Drouin, MD, Co-Founder and CEO at Clarify Health
Improving health equity in the United States will require an investment in exploring and leveraging non-clinical data. It turns out, if you don’t have access to food, if you don’t have access to transportation, or if you live on your own, those can be risk factors or predispositions for not being able to go through a journey of care as well as someone who has access to food, transportation, and support at home. Leveraging these non-clinical factors, known as social determinants of health (SDoH), in combination with claims and traditional clinical data, can uncover the root causes of systemic disparities, identifying where health equity efforts fall short and enabling key stakeholders to develop a stronger, data-backed course of action that enhances patient engagement strategies and leads to better patient outcomes.
Joni Orand, Senior Solution Engineer and Consultant GRC, CQS at symplr
The shift to value-based payment in healthcare links reimbursement to outcomes – as a result, payers and provider organizations are incentivized to act on data related to social determinants of health (SDoH). Patients do not always think about their risk factors, such as education, poverty, safety or living conditions, transportation, etc., for health outcomes in the same terms as healthcare organizations and regulatory bodies do, which makes providers leveraging non-medical factors and conditions while caring for patients crucial to ensure proper quality, safety, and reimbursement measures are met. Incorporating solutions into healthcare operations with health equity and equality in mind will help provider or payer organizations track and incorporate SDoH data today, resulting in the potential for improved health outcomes down the line.
Susa Monacelli, General Manager at Propeller Health
Digital health is an immensely powerful tool for enabling personalized care and optimizing the right intervention, at the right time. Thoughtfully-collected data may allow for a more holistic patient experience, while also providing important insights to providers and payers. With higher quality objective insights, providers will be empowered to make more informed treatment decisions with patients, and payers can effectively evaluate care outcomes and apply learnings more broadly to a variety of patient populations, from pediatric to geriatric to those historically underserved.
Oleg Bess, Co-Founder & CEO at 4medica
Accurate data is key to improving health equity. This is important both on a broad scale and for individuals. Government agencies and healthcare organizations need comprehensive data to determine whom inequity affects and how. Patients dealing with health inequity have more access than ever to their personal data, but it must be complete and accurate if they are to be able to make the best healthcare decisions for themselves. Therefore, it is incumbent on providers, payers and government agencies to ensure their data is of the highest quality in order to further the efforts to improve health equity.
Kinte Ibbott, Vice President at Maximus
Data are a cornerstone for efforts to address disparities and advance health equity. It is essential for identifying where disparities exist, directing efforts and resources to address disparities as they are identified, measuring progress toward achieving greater equity, and establishing accountability for achieving progress. Without adequate data, inequities remain unseen and unaddressed.
What great insights! Thank you to everyone that submitted a quote and thank you to all of you for reading them! Be sure to tell us what you think about the role data plays in health equity in the comments down below and on social media. Plus, watch for our next health equity articles where we’ll look at technologies that help with health equity and new angles and approaches to health equity.
