Intermountain Healthcare: The Data-Driven Healthcare Pioneer Praised by Obama and Outperforming Kaiser Permanente

There is an anecdote recounted by a physician himself: Throughout human history, medical treatment has, more often than not, caused more harm than good. “If there were no physicians, allowing diseases to run their natural course, both suffering and mortality would be reduced,” stated the renowned physician Jacob Bigelow in 1835.

With advances in medical science, human life expectancy has been significantly extended, making such statements less applicable in the modern era. Nevertheless, it is undeniable that physicians throughout history have largely relied on their knowledge and clinical experience to treat patients, often constrained by memorable past cases, leading to diagnostic decisions that were relatively subjective and intuitive. In recent years, the wave of big data has swept through the healthcare sector. Physicians have begun to acknowledge their cognitive limitations, shifting from exercising sole authority over patients’ lives and deaths to delegating decision-making power to objective data, with “evidence” becoming the primary basis for treatment judgments.

In the data-driven transformation of the healthcare industry, Intermountain Healthcare, a non-profit health system in the United States, has led the way, with achievements that even surpass those of Kaiser Permanente, which has long been a leader in healthcare model innovation. VCBeat (WeChat ID: vcbeat) takes you through Intermountain Healthcare’s journey of leveraging data to transform healthcare.

Intermountain Healthcare was established in 1975 and is headquartered in Salt Lake City, Utah. It is the largest healthcare provider in the Intermountain West region of the United States. The former administrator of its hospital system, The Church of Jesus Christ of Latter-day Saints, announced in 1974 that it would donate its 15 hospitals to the Intermountain community, thereby giving rise to Intermountain Healthcare, a nonprofit organization that continues to provide medical services to the region.

In the mid-1990s, Intermountain Healthcare gradually restructured into three major groups: a hospital group, a physician group, and a health insurance group. This restructuring laid the foundation for Intermountain Healthcare’s implementation of the American Health Maintenance Organization (HMO) model within its system. Currently, Intermountain Healthcare’s hospital group manages 22 hospitals and 185 clinics in Utah and Idaho. Physicians within the physician group also operate numerous urgent care centers. The group owns the insurance brand SelectHealth, which provides comprehensive health management services, with its insurance business accounting for 22% of the total market share in Utah.

In terms of medical services, a major innovation by Intermountain Healthcare is its air ambulance unit, known as Intermountain AirCare, which originated in 1972 (predating the establishment of the Intermountain Healthcare Group). It serves western U.S. states including Utah, Arizona, Idaho, Montana, Nevada, and Wyoming. The unit currently operates five helicopters and three fixed-wing aircraft, providing air transport services for both emergency and non-emergency patients in remote areas, as well as search and rescue services within the aforementioned regions.

In scientific research, Intermountain Healthcare is an undisputed leader. Its cutting-edge medical exploration dates back to the 1950s, when its flagship institution, then known as LDS Hospital, had already initiated formal biomedical research. Over the decades, Intermountain Healthcare has conducted thousands of studies across dozens of fields, with more than 1,400 active research projects currently underway. Its ten core research areas are behavioral sciences, cardiovascular care, critical care medicine, musculoskeletal care, neuroscience, oncology, pediatrics, primary care, surgical services, and women’s and newborn care. Notably, its cardiovascular research database is the largest in the world.

Over the past three years, Shanjian Healthcare conducted second-generation targeted gene sequencing on 700 patients with advanced-stage cancer, analyzing mutations in 96 cancer-related genes. In early October this year, they established a new company, Navican, dedicated to genomic testing research, with a focus on personalized disease prevention and customized diagnostic and therapeutic services for patients with advanced-stage cancer.

In the realm of medical education and dissemination, Intermountain Healthcare also holds a position of authority. The group operates a “Healthcare Transformation Training Center” designed to train industry leaders from around the world, enabling elite professionals to learn advanced healthcare models and thereby improve the quality of care at their respective institutions. Recently, Kem Gardner, a philanthropist from Utah, donated $20 million to Intermountain Healthcare to establish a new Healthcare Transformation Training Center.

In 2014, the American Recovery and Reinvestment Act called for all healthcare institutions to implement Electronic Health Record (EHR) systems to maintain eligibility for Medicaid and Medicare reimbursements. However, as early as the 1970s, Intermountain Healthcare had already developed its own EHR system, earning it a pioneering reputation in “evidence-based medicine.”

It was from this point onward that a data-driven mindset became widely entrenched among the physicians and administrators at Shanjian Medical: every clinical project was supported by a dedicated data analytics team; procurement decisions for medical supplies were heavily influenced by data analysis; and communication between healthcare providers and patients was continuously enhanced through the support of medical data, with public health data and patient feedback meticulously analyzed to drive improvements in practice.

Mountain Healthcare Group’s Electronic Data Record Culture Has a Long History

Today, their team of data analysts has grown to over 2,000 members, who make sense of the vast amounts of data generated daily by electronic health records (EHRs) and contribute better solutions to every detailed process within this large healthcare group. By leveraging medical big data as evidence, Shanjian Medical has achieved improved patient outcomes across its cardiovascular medicine, endocrinology, surgical, obstetrics, and entire nursing care workflows, while also optimizing its procurement supply chain and saving millions of dollars.

In the 1980s, Alan Morris, a pulmonologist at Intermountain Healthcare, received a research grant to investigate whether mechanical ventilation was effective in treating acute respiratory distress syndrome (ARDS). Aware of physicians’ tendency to adjust ventilator settings based on their own clinical judgment, he developed an “ARDS Treatment Protocol” to standardize operational procedures. However, clinicians remained skeptical about the protocol’s efficacy. To demonstrate its scientific validity, Morris designed several alternative operational protocols for comparative studies during patient treatment. The trial required physicians to strictly adhere either to the ARDS Treatment Protocol or to one of the alternative protocols provided by Morris, ensuring consistency in practice throughout the study.

While physicians conducting controlled-variable experiments on the ARDS treatment protocol, Intermountain Healthcare’s electronic health record (EHR) system was tirelessly documenting patient outcomes under different procedural approaches. Every week, a group of pulmonologists convened to review treatment efficacy recorded in the EHR, thereby assessing the validity of each provision within the protocol. Evidence showed that Morris’s ARDS treatment protocol was riddled with errors; however, the approach of strictly defining treatment methods based on real-world data was undoubtedly correct. Through iterative feedback from experiments, they continuously refined the protocol, ultimately arriving at the relatively most scientific “New ARDS Protocol.” What shocked those physicians who had initially opposed using protocols to constrain clinical practice most was this: a subsequent nationwide study revealed that the ARDS cure rate in the United States at that time was approximately 10%, whereas at Intermountain Healthcare, the figure stood at 40%.

The pulmonologists on the research team were truly eye-opened by this achievement. They not only recognized the practical efficacy of using data-driven evidence to standardize treatment protocols, but were also amazed at having completed such a complex endeavor in guideline development.

Since the inception of its ARDS research, various departments at Intermountain Healthcare have progressively adopted the practice of using feedback data from ongoing treatments to develop clinical protocols for a wide range of diseases. To date, standardized treatment protocols have been established for more than 50 complex clinical conditions, and half of Intermountain’s patients receive care guided by these protocols. For each disease-specific protocol, a committee comprising physicians, nurses, and administrators from relevant departments across Intermountain’s network of 22 hospitals is formed to identify and document effective and ineffective therapeutic interventions.

Once the treatment protocol was established, the committee extensively disseminated its core principles to medical staff in the relevant disease-specific departments, ensuring their proficiency in the most effective operational procedures and emphasizing that the protocol’s importance superseded that of any other documentation. Standardized guidelines were also integrated into the hospital’s computer system, providing automated recommendations for issues such as appropriate medication dosages for specific conditions.

Although modern individuals can access substantial disease-related information through advanced online resources, a physician’s diagnosis remains akin to an imperial decree for patients who are desperately seeking hope. Unfortunately, most medical decisions stem from physicians’ empirical intuition. Issues such as whether a treatment is effective, or whether a therapy that works for some patients can be applied to others, are unavoidable challenges within the realm of “experience-based medicine.” Furthermore, the prevailing practice in the healthcare market dictates that the more tests performed and the more cumbersome the treatment process, the higher the medical costs. Physicians’ income is tied to the volume of tasks completed rather than the quality of clinical outcomes. Consequently, physicians’ judgments may subject patients to unnecessary tests and treatment procedures.

Mountain Healthcare, having implemented electronic medical records (EMRs) early on, could readily identify issues through treatment outcome data. Consequently, it rejected the somewhat absurd “fee-for-service” reimbursement model that rewards volume over value. Instead, the organization provided most physicians with fixed salaries and leveraged data-driven performance assessments to link clinical efficacy with compensation. Physicians achieving superior treatment outcomes received bonuses, thereby incentivizing the adoption of the most effective, rather than the most cumbersome, therapeutic approaches. Both the committee members responsible for drafting disease-specific legislation and practicing physicians could access patient treatment outcomes via EMRs. Physicians with consistently poor performance underwent analysis to identify flaws in their treatment protocols and were urged to make corrections, while those with sustained high performance had their effective practices analyzed and promoted.

However, while the concept of revenue driven by therapeutic efficacy is more patient-centered, it poses challenges in establishing standards for evaluating medical outcomes. Some assessment criteria adopted by the Intermountain Healthcare Committee were developed based on internal experience and differ from externally published research findings, sparking controversy among physicians. For instance, some doctors have pointed out that the Intermountain Healthcare Committee incorrectly set the optimal level for glycated hemoglobin (HbA1c), a common blood glucose indicator for diabetes, at 8%. Under this standard, a reduction in a patient’s HbA1c from 13% to 9% after treatment is already considered highly effective, especially given that “some reports even suggest 9% as the optimal level.”

Ultimately, what constitutes effective treatment outcomes and how to quantify physician performance cannot be fully captured by a few numerical metrics; rather, it requires case-by-case analysis.

Wennberg, a researcher from Dartmouth, argues that Intermountain Healthcare shares many similarities with other frequently cited high-quality, low-cost healthcare systems such as the Mayo Clinic and the Cleveland Clinic. For instance, most physicians in these systems receive fixed salaries rather than compensation based on workload. As a result, physicians are not incentivized to order excessive tests, treatments, or procedures, yet these systems still achieve favorable outcomes.

A reduction in treatment steps corresponds to a reduction in healthcare costs. Barack Obama once praised Intermountain Healthcare as a model for the healthcare industry: “We have long known that certain places, such as Utah’s Intermountain Healthcare… deliver high-quality care at below-average costs.” According to a survey by the Kaiser Family Foundation, per capita healthcare spending in Utah is 44% lower than the national average, with Intermountain Healthcare playing a significant role in this achievement.

However, Wennberg believes that Intermountain Healthcare is fundamentally different from other healthcare systems. What sets it apart is its rigorous analysis of clinical care data and its commitment to improving practices. “Intermountain’s advanced data analytics network represents the best model in the United States for truly improving the healthcare industry,” the analyst commented.

By establishing evaluation standards for data and treatment outcomes, Intermountain Healthcare has achieved significant results. The preterm birth rate and the proportion of newborns admitted to intensive care units have both declined; since the mid-1990s, adverse drug reactions, including overdoses and drug allergies, have been cut in half. Treatment protocols developed for a major category of pneumonia reduced mortality from this disease by 40% within just a few years. The mortality rate for coronary artery bypass grafting has dropped to 1.5%, compared with the national average of 3%. Data from Medicare show that readmission rates for heart failure and pneumonia patients at Intermountain Healthcare are significantly below the national average. It is estimated that, through the use of data analytics, the Intermountain Healthcare network saves thousands of lives each year.

At the “Washington D.C. Health IT Summit” held on October 25 local time, Greg Poulsen, Chief Strategy Officer of Intermountain Healthcare, shared his insights on the future direction of the healthcare industry:Enhancing Healthcare Quality and Value Through a Data-Driven Approach.

Chief Strategy Officer Greg Poulsen at the DC Health Information Technology Summit

In his speech, Poulsen advocated for the concept of capitation, a prepaid healthcare model in which health insurance and medical services complement each other, placing greater emphasis on prevention rather than treatment. “The concept of capitation carries far deeper significance than merely ‘medical costs’: this is what ideal healthcare should look like.” He also quoted from Clayton Christensen’s new book, Racing Against Luck: The Story of Innovation Behavior and Consumer Choice: “The high-quality healthcare that most people aspire to is one where everyone maintains excellent health and never has to think about how to treat illness; yet in a system where reimbursement is tied to the volume of services provided, healthcare providers only make money when people are sick—this is more akin to ‘sick care’ than ‘health care.’”

Poulsen pointed out that the MACRA (Medicare Access and CHIP Reauthorization Act) legislation, announced in early October, is driving major healthcare organizations to transition from fee-for-service to prepaid care models. However, to succeed in prepaid care or performance-based incentive compensation models, healthcare providers must leverage data, information, and analytics technologies to improve treatment outcomes and resource utilization.

Furthermore, Poulsen noted that treatment protocols for the same disease vary significantly among physicians. However, if data demonstrate that a standardized approach offers optimal clinical efficacy and cost-effectiveness, it would spark discussion among clinicians, thereby leading to greater uniformity and scientific rigor in treatment practices.