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The heath care landscape is at a crossroads. Clinicians are faced with increasing pressure to improve treatment outcomes while simultaneously reducing costs, and as such, many organizations are seeking to create new operating models that foster and align decision rights to the care teams. It has become very clear that the right information at the right time empowers care teams to make optimal decisions, and ultimately supports the transformation to a data-driven culture.
"Care teams are now demanding data in their quest to achieve clinical outcomes at lower costs "
From the implementation of EMRs to mobile patient services platforms, technology and big data have been slowly infiltrating internal processes and workflows across health care, but many organizations have yet to harness its full power. Analytics programs should be designed to do more than identify insights; they should apply the right insights across the key levers of performance, providing the context and evidence necessary to inform clinical decision-making and strategic direction. The right solution will minimize inefficiencies and care variations in a way that improves not only the patient care journey but also the bottom line.
However, without the engagement of health care providers, organizations may suffer the same fate as extinct software systems. For a program to be successful, an organization needs to reconsider its mission, resources, internal structure, technological architecture, data application, and culture. For organizations holding on to traditional hierarchical models built around siloed departments and workflows, this creates a conundrum that often demands a comprehensive overhaul.
A team of team –a single patient-oriented mission
To start, it is necessary to create an organizational structure that supports autonomous decision-making under a collective mission. Consider Chris Fussell’s “team of teams”—a network of small, cross-discipline teams that enables agility without sacrificing the power and scale of a large enterprise. These types of organizational models support the capabilities and goals of individual teams, as well as the overall enterprise. It is empowering clinical teams to focus on impactful initiatives that improve patient care, while reducing and unnecessary variation, without hampering them down with slow, impudent bureaucratic processes. In today’s beleaguered healthcare system, burdened with epidemic levels of stress, depression and burnout, this is a welcome boost to tired clinical teams looking to be energized with the passion of what drew them to healthcare originally—caring for patients.
Many of the most effective teams are structured around discrete patient care populations that have similar needs and a dedicated team of caregivers. This approach fosters cross-departmental collaboration and assuages historical tensions by focusing on the patient, which eliminates blame shifting. Important multidisciplinary members include technology, finance, and clinical analytic resources. These teams are also better positioned to address challenges across the continuum of care within the industry’s Quadruple Aim framework— defined as the convergence of an enhanced patient experience, improved population health, reduction of costs, and improved work life for clinicians.
Population-centered teams align well with the need for teamwork and the engagement of clinician’s passion to improve outcomes for patients that they care for. These teams constitute the organization’s clinical expertise and are best positioned to gain insights from clinical data and the adaption of evidence based practices. It is at this venue, with the opportunity to identify innovations in care and the subsequent hard wiring of care components into enabling technology, where sustainability is achieved.
Data-first patient care
Introducing data into the equation adds a layer of multifaceted challenges. The leadership of the organization will drive expectations around data, how and where it can be used, and the resources and training invested in leveraging it as a strategic asset.
The organization must foster a data-forward mindset and enable the democratization of data and insight as much as possible. Knocking down the hierarchical barriers that traditionally surround data empowers data-literate clinical teams to utilize on-demand data to enable more effective, autonomous decision-making. Teams should test hypotheses, judge experimental design, and interpret metrics to share insights, therefore opening the door to an environment of continuous discovery.
Impactful analytics requires access to timely, accurate data that is relevant to clinical practice. Clinical teams should collaborate with analytics to prioritize the best data sources for the overall organization while minimizing collection bias and maximizing data quality—something that has been challenging to achieve. From a stakeholder perspective, the value of systems founded on the exchange of real-time data lies in the ability to track processes, outcomes, and financial measures to build a sustainable, cycle of continuous improvement. One that, if built strategically, will be both repeatable and scalable. After all, they are there to inform the future of the patient care.
In the end, it will not matter how new payment and care models evolve; organizations that provide the best care for patients in the most cost-effective way will come out on top. The misconception that higher costs translate into higher quality care is extinguished when care providers can use data to inform their practice, providing the right treatment at the right time in the right setting. Care teams are now demanding data in their quest to achieve clinical outcomes at lower costs—establishing a data-driven culture can be the difference between a “good” healthcare organization and an outstanding one.
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