Health care has become a data-driven business. It is no longer acceptable that both incoming and current health care professionals and business leaders lack an understanding of the influence data has on health care delivery. The clinician coauthors listed here represent this sphere and are still learning every day. We represent the diverse background of professionals that exist in the health data space, with a wide variety of journeys into this arena [1]. “Health data” is a broad term, often referring to data collected and exchanged in electronic systems. Everyday health data are entered, exchanged, and used to make important decisions from the patient level to the systems level. Health care professionals today need an understanding of the utilization and impact of health data to optimize care delivery and interact with the many systems they encounter daily.

When we entered the health care industry over 20 years ago, we were hopeful clinicians excited to impact patients’ lives. For many of us, we quickly became disillusioned by a system that was driven not by patient outcomes but by reimbursement. Yet, we regained hope with pivotal moments, including when Don Berwick challenged health care organizations to promote quality and evidence-based medicine with the Institute for Healthcare Improvement; the proliferation of electronic health record (EHR) usage leading to the potential to share patient information across systems [2-4]; and the opportunity to move from fee-for-service to value-based payment [5]. We continue to grow in hope, as many openly discuss health equity and social determinants and drivers of health. In addition, the conversations and investments in workforce related to health data knowledge and expertise are ongoing and receiving national attention. Opportunities abound with the expansive growth of artificial intelligence and machine learning.

However, none of these impending innovations can grow and disseminate without understanding data. Gaining an understanding about health data and their use by clinicians is critical to promote the key structural aspects necessary to improve health care delivery, including interoperability, data standards, quality measures, and reimbursement for health outcomes. When we started in health care, the understanding of the impact of health data did not truly and widely exist. In our experience, we find that many clinicians are unconsciously incompetent—lacking a basic understanding of how health data are used, what health data consist of, and where data flow [6]. Unconscious incompetence occurs when the decision makers lack the true information and expertise needed to make an informed decision [6]. This lack of competence causes uninformed decision-making in the health care ecosystem, which causes more challenge. Technology and data become a burden and not a solution.

In health care, a health data–informed workforce is needed to remedy the gaps and make the important connections for positive change. In our experiences and those of our peers, we interact with clinicians who learned about informatics and health data on the job [1]. Many stories start with an interest in data and technology or some savviness with technology. These are individuals willing to lean into innovation and learn through failure. Yet, their learning curve is steep and lacks the efficiency that a health data–informed workforce could address. The understanding of health data has become a shared team value critical to growing and expanding the evidence to support interprofessional practice. Now is the time to move beyond the early adopters and explore how we can expand the health data–informed workforce. We acknowledge previous authors that have called for this momentum to grow and call for ongoing and widespread engagement. In this viewpoint, we attempt to define the health data–informed workforce at the micro-, meso-, and macrolevels. We then offer suggestions for clinicians wanting to level up their competence in health data.

A health data–informed workforce includes clinicians with a basic understanding of data along with their exchange and influence on decision-making. The ideal would be to move clinicians from being unconsciously incompetent to consciously competent. However, the amount of knowledge expected is overwhelming. The complexity of health data has evolved into the field of health informatics. Multiple studies have indicated that the field of health informatics is diverse, with a wide variety of education and workplace requirements [1,7,8]. Health informatics is a field that explores the use of health data for “scientific inquiry, problem-solving, decision making” with the intent to improve health care delivery and impact [9]. Yet, health data impact every level of health care, from the micro- to macrolevel, calling upon all clinicians to hold a basic understanding.

For the purposes of this viewpoint, we consider the microlevel to be interactions with patients and clinicians; the mesolevel focuses on the infrastructure and systems in place for health data sharing; and the macrolevel addresses the impact of policy on health data. Clinicians who are data informed at each of these levels will improve the impact of health data utility and ensure that decisions made around health data and technology will facilitate positive change.

At the clinician and patient level (ie, the microlevel), data are used to make clinical decisions. The widespread adoption of EHR systems supported by the 21st Century Cures Act and provisions against information blocking in the Office of the National Coordinator of Health Information Technology’s Final Rule place a premium on data, and data literacy, in health care delivery [10]. The availability of data and the ability for patients to access their health data through patient portals and other digital applications can advance shared decision-making, promoting improved health outcomes while empowering patient’s involvement in their care. Yet, EHRs have introduced burden, and many clinicians are under information overload, which can result in health care errors [9,11]. A recent piece in the Journal of the American Medical Association titled “Death by Patient Portal” illustrates the love-hate relationship that occurs with much of health technology and data [12]. Data are flowing and being shared, but questions remain on how much and how to make information usable for patients and clinicians. Despite these challenges, data and technology are reported to only continue to grow in health care. This calls on clinicians to know how to access patient data in their EHRs, understand where patients track and record data, and feel comfortable translating health information to multiple levels of digital and health literacy. A health data–informed clinician knows to use tools such as health information exchanges (HIEs) to ensure that they are making clinical decisions with comprehensive patient data beyond the EHR [13]. An HIE extracts data from multiple EHRs and matches that data into a comprehensive patient record. In some health care organizations, HIEs are integrated into the EHR. They can provide quick and comprehensive patient data for clinical decision-making [14]. Due to HIEs, health care becomes more proactive and less reactive when clinicians are aware of a recent emergency department visit, for example. At the same time, HIEs can lead to information overload for providers. In addition, clinicians improve their patient experience when they have information about the patient journey and history, which an HIE can provide [15]. Patients also report a better patient experience when they are not forced to “repeat their story” or re-enter information they have already reported.

Many health care organizations use data for various reasons, including use by health care delivery systems, payers, and academic researchers. When it comes to the mesolevel, the health data–informed workforce needs to understand data governance, including understanding how, why, and when data are shared and recognizing the importance of privacy and security. Patient consent remains important to ensure patients know when and where their data are being shared and how they are being used. In addition, health technology selection and vetting, along with vendor management, is critical. Vendors can offer solutions, yet at the same time, these tools can have unintended consequences from data being entered into multiple systems, causing burden on clinicians and a lack of data completeness in a patient’s record. Clinicians need to recognize the importance of interoperability as it impacts data access and use between systems. Interoperability refers to the ability to exchange data in a useful manner. An interoperable approach reduces double documentation and siloed health data [16]. At the same time, health data have extensive protections under the Health Insurance Portability and Accountability Act (HIPAA), which requires thoughtfulness to the exchange and use of data across systems. We have witnessed too many clinicians enamored with a piece of technology without vetting its ability to further health care. Great technology that further siloes data into multiple systems and lacks expanded adoption can cause more burden and potential patient harm. We need a workforce that questions the benefits and challenges how additional technology and data can actually improve health care delivery along with their interoperability.

Health data–informed clinicians also recognize the importance and value of data standards [17]. Data standards provide a critical foundation for data exchange. Decisions are being made daily in health care organizations without the recognition or use of data standards. One example is choosing to create a health-related social need screener without considerations of existing tools or data standards work, such as that led by the Gravity Project [18]. These approaches further denigrate the system and cause a myriad of challenges to interoperability.

For the macrolevel, understanding and advocating for local and federal policies that support the proliferation of growing workforce expertise is critical for the health data–informed clinician. Clinicians need a basic recognition and understanding of how policy drives health data utility [19]. The gaps in the workforce around health informatics have been identified and acknowledged [7,20,21]. Efforts in this area have been made by the American Medical Informatics Association’s 10×10 program and the federal funding to support the Public Health Informatics and Technology Workforce Program by the Office of National Coordinator of Health Information Technology. These policies and investments provide opportunities to support both current professionals and those entering the workforce, representing examples of the impact of policy on health data.

Our hope is to promote a conversation and spark innovation around the need to expand and grow the health data–informed workforce. We certainly cannot provide every piece of advice or suggestion here. Yet, we hope to spark a revolution to grow the cadre of passionate advocates for the proliferation of health data and technology in ways that truly support equity, reduce burden, and improve health care delivery. Additionally, we are not saying that data skills are not critical—they are. We recognize that we need more than that. We need a workforce that asks questions about where data go and how they are used and that becomes more informed on the data tools of their patients. We need a health data–informed workforce now and into the future.