From EMRs to data integration platforms

Supporting data-driven decision making in healthcare

Preview

Healthcare is increasingly becoming an information industry. New applications and technologies are generating data on an unprecedented scale and pace and are challenging the cornerstone positioning of electronic medical records (EMRs) in current and future digital healthcare ecosystems. This article champions the integration of EMR systems into health data platforms as pathways to healthcare sector transformation. Examples of how healthcare organizations around the world are leveraging data for enterprise-level decision making are also showcased.

Electronic medical records are, for many people, synonymous with digital health. Since their inception in the 1970s, they have evolved from payment management systems to digital repositories of patient data, to multi-functional systems that offer services such as clinical decision support. New technology has brought artificial intelligence (AI) to the EMR as the latest addition in a large collection of functionalities for doctors, nurses and other medical professionals.

Today, a number of factors are challenging the cornerstone positioning of EMRs in current and future digital healthcare ecosystems. Healthcare is increasingly becoming an information industry. New applications and technologies are generating data on an unprecedented scale and pace. A proliferation of patient data from sources outside of clinical settings — such as consumer wearables, a patient’s genomic data or lifestyle information — means the classic EMR only provides a partial picture of people’s health. The pace of innovation in the wider digital health space is accelerating such that EMR systems just cannot move fast enough to incorporate the latest developments. The era of monolithic EMR systems is moving towards a new era in which data and functionality will be separated. With better data accessibility from a wider collection of resources, innovation in healthcare is poised to accelerate.

While these developments may pose challenges to EMR systems, there is also an opportunity. Instead of striving to be one-stop shops for digital healthcare within a particular hospital or clinic, EMR systems should be integrated into health data platforms that transcend individual healthcare organization settings. These platforms can pull information from relevant sources; for example in the US, the Centers for Disease Control and Prevention’s Places program provides health data for small areas, which helps local health departments “to better understand the burden and geographic distribution of health measures in their areas” and also assists with public health intervention planning.¹ When these platforms pull information from all relevant sources — from providers and their partner organizations and from consumer applications — they can provide paths to healthcare sector transformation in several directions, including the creation of regional- or even national-level digital health ecosystems that use data to inform service design and workforce planning, population health strategies and individualized care experiences for patients. In the face of spiraling demand and costs for healthcare services, having accurate, up-to-date health data and these interoperable capabilities has never been more critical.

Author

Hylke Kingma

Partner, Digital Health
KPMG in the Netherlands

Data-informed decision making in action

Leading healthcare organizations around the world have begun to leverage data for enterprise-level decision making. Below are a few examples:

Making better use of technology to help improve care in the Netherlands

Since 2018, Gelre Hospitals in the Netherlands has been working to make better use of digital health technology in delivering care. The hospital group has worked with KPMG in the Netherlands to create a multi-year strategy and digital roadmap that included a migration to public cloud and setting up the infrastructure and governance for a new cloud-based data platform capable of integrating data from EMRs and multiple other sources. This platform has established a strong foundation to generate valuable insights for patient care.

One of the developed use cases for this data platform is predicting intensive care unit (ICU) inflow. The primary goal of this project that is under development is to create an accurate and reliable predictive model to forecast ICU inflow. By anticipating patient demand, the healthcare system can help optimize resource allocation, including staffing, equipment, and facilities, thus helping to enhance patient care and improve operational efficiency.

A better prediction of the number of patients coming to the ICU can help with more flexible scheduling, allowing you to give staff rest when possible and deploy additional staff when necessary. To this end, it is important to have a good insight into all patients who are treated in the hospital and who are at risk of deteriorating. A lot of important information is recorded in the electronic patient file, but it is impossible for the healthcare professional to continuously monitor this for all patients. This is where artificial intelligence can help us, by providing a timely warning and filtering the essential data needed for forward planning. This allows us to provide better care for more patients and prevent staff from dropping out due to overload.

Nicolas Schroten
Intensivist
Gelre Hospitals

Increasing patient access in the US

A large regional healthcare system in the US was facing a number of challenges that included the need to further centralize patient registration capabilities at its ambulatory/outpatient locations, optimize healthcare provider schedules, improve patient triaging processes and expand digital care pathways and appointment scheduling.

The organization worked with KPMG in the US on an innovative approach to help improve experiences for patients, create a more efficient working environment for staff, and increase revenues. After establishing sustainable access governance, practice operations and specialty working group structures, data from the organization’s EMR system was plugged into an access analytics tool created by KPMG in the US in order to centralize scheduling, registration, and registered nurse triaging for multiple specialties.

Patient visit types were rationalized from roughly 300 to 13 types and multiple specialties were aligned to standard visit timeslot durations, which reduced the average visit duration by roughly 10 percent without impacting provider time with patients.

Regional-level digital health ecosystems in the US

In the US, the New York eHealth Collaborative is tasked with improving healthcare outcomes by jointly leading, connecting and integrating regional information exchanges into a statewide health information network. KPMG in the US has enabled trust in the statewide health information network and the regional health organizations through the performance of annual assessments of the regional health organizations and their adherence to shared policies around privacy, security and core system functionality.

Regardless of where someone receives care, their basic care information is always available — no matter which EMR system is being used. The network has enabled the state-wide lookup of patient records, alerts and notifications and other functionality, which is now integrated in the workflows of healthcare providers across the state. The latest initiatives are integrating social, welfare and housing information with the healthcare data to allow for a more holistic perspective of each patient as well as population health management initiatives. Marrying this information could help organizations to target interventions to groups with chronic health conditions or those at high risk of being impacted by extreme weather conditions, thus helping to lower healthcare demand and care costs.

Future digital health infrastructure

We anticipate that the digital health infrastructure of the future will demand as much hands-on management as other functions of a health system. Rather than being contained in one application, we foresee it will be an ever-evolving landscape of applications layered over health records, extracting data, analyzing it, and feeding it back into the health data platform which contains standardized data models. The importance of this is reiterated by KPMG research² which showed that healthcare is the sector most likely to say that AI and machine learning (ML) systems are going to be strategically important over the next three years: 62 percent of healthcare organizations considered AI and ML to be the most important technologies for achieving short-term ambitions — 5 percentage points above the average across all industries surveyed. In line with this finding, we predict that the proper use of data platforms will likely accelerate innovation, deliver clinical insights and optimize the overall delivery of care.

The (technical) realization of data platforms requires a holistic approach. Making choices among the wide range of digital care and platform technologies can be difficult. Implementation also often poses a challenge. Around the world, KPMG professionals have deep healthcare industry knowledge and experience in helping healthcare organizations implement data-driven approaches. We feel that digitalizing is not only about putting down the technology and tooling, but also requires attention to and change management in the areas of governance, processes, people and culture.

Healthcare organizations key performance indicators

Key takeaways

Healthcare is increasingly becoming an information industry. Healthcare organizations should digitalize to take advantage of the vast amounts of data from inside and outside of clinical settings to help improve both patient and operational outcomes.

Separating data from functionality is key for creating an innovation-friendly digital health infrastructure. In such a system, the data platform becomes the backbone of the digital health ecosystem. Bringing data together requires careful planning and governance.

Data platforms can provide paths to healthcare sector transformation in several directions, including the creation of regional- or even national-level digital health ecosystems that use data to inform service design and workforce planning, population health strategies and individualized care experiences for patients.

The realization of data platforms requires a holistic approach. Beyond technology and tooling, particular attention should be paid to change management in the areas of governance, processes, people and culture.

How can KPMG help?

KPMG firms have deep knowledge in business technology. Our award-winning^3,4,5,6 transformation, innovation, and profound industry know-how positions KPMG professionals to address market challenges and provide in-depth industry perspectives. We use technology in ways that can help increase competitive advantage for healthcare organizations, such as leveraging the KPMG Connected Enterprise for Health framework, deploying digital health solutions to address concrete issues and employing our strong network of alliances with some of the world’s leading technology, data and services companies. KPMG professionals have the ability to meet organizations where they are at in their transformation journeys, whether that is helping them to successfully adopt appropriate solutions, increase innovation and/or embark on broader digital transformation. Get in touch to learn more about how KPMG can support transformation in your organization.