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Recent legislation has forced health care organizations to re-evaluate current payment models and discuss the shift to value-based care. As healthcare entities begin this transition, many are pursuing clinical integration as a means to prepare for the change. Clinical integration can provide a bridge to these early models by providing a structure that supports greater collaboration and care coordination. While clinical integration can take many different forms, it generally requires the sharing of evidence based processes, clinical benchmarks and the creation of a common understanding of the patient, their diseases, and the care management strategies being employed by a team of physicians across the spectrum of care.
Clinical integration builds on the experience and progress that many organizations have already achieved. This approach will also require new investments in technology and infrastructure. At its most basic level, technology will drive greater connectivity of patient data to support a team-based approach to care. However, simply sharing immense volumes of patient data has already proven to be neither efficient nor productive in delivering lower costs and improved patient care. For example, rather than looking at a diabetic patient’s entire medical history, providers may want to quickly identify meaningful lab values and clinical benchmarks. At a population level, entities will also require actionable data to identify at-risk individuals and drive targeted interventions. Data is a primary asset of the new healthcare organization, resulting in a platform on which to build the required improvements in processes and resulting outcomes. Creating organizational culture that exploits data-driven clinical intelligence will determine success or failure in the new clinically integrated models.
Workflow technologies can drive more efficient, collaborative care
Emerging workflow tools can support these needs by offering a flexible, data-driven method to improve care delivery. These solutions integrate and rationalize clinical data from patient encounters, lab and pharmacy plus patient demographic and financial data from billing and claims without losing the original context and meaning. This data is then refined into patient profiles that are matched with evidence-based clinical standards to find gaps in care and opportunities to improve practice patterns and treatment.
These new technologies will enhance physician efficiency, empowering providers to spend more time interacting with patients and addressing clinical concerns. These systems also allow for a more personalized and holistic, patient-centered care that considers each individual’s unique needs. Once this advanced insight is made available, it can be used to predict future care needs and enable office-based care coordination efforts. For example, the sharing of real-time data will drive dynamic care/transition care plans. This approach will give providers immediate access to accurate clinical data for patients at risk for adverse events and development of new comorbidities.
Driving greater patient engagement
Advanced technology must also plug into patient engagement strategies to achieve the triple aim. Advanced patient engagement solutions educate and support a patient’s wellness lifestyle, encourage individuals to access the health care system efficiently and effectively, and empower effective in-home management of chronic disease. By using technology to inform and guide patients to the most appropriate care, organizations can optimize utilization, improve outcomes and reduce network leakage.
Online and mobile applications provide an ideal vehicle for these strategies. For example, e-technologies can give patients access to health records, appointment setting and personalized health advice. For patients with chronic conditions, mobile/online tools can also provide peer support and disease-specific education. Patients who are part of clinically integrated networks have access to mobile tools that not only help identify the cause of a sickness, but also recommend the patient visit a retail clinic or physician office versus the emergency room to save time and money. Together, these resources will help individuals take an active role in their own health and drive lasting behavioral changes.
Supporting data-driven population health strategies.
Success of accountable care models and the clinically integrated networks which form their foundation are based on proactive management of large populations of patients. This proactive care management includes not only those that are actively “in front” of clinicians in a care setting but, more importantly, the ones who are rarely seen. When a provider is assuming risk for a population, they are accountable for patients they have minimally seen as well as the ones they routinely see. They need to gain insight into their entire population.
Three key areas of focus for population health analytics include:
1. Understanding the patient population and risk profile. New technologies support population health improvements by offering analytics that extend beyond the point of care. These systems can provide detailed views of population health down to the practice and individual level. As a result, entities can stratify high-risk patient populations and develop a better understanding of these segments. What’s more, these advanced analytics can help identify which patients are more likely to respond to a given intervention, allowing for more targeted outreach.
2. Finding and addressing potential gaps in care. Solutions to provide the evaluation and consolidation of best medical evidence, and building decision making and workflow tools to support delivery of this care will be critically important in these new delivery models. By providing a clinician access and patient-specific comparisons to evidence-based protocols and care pathways, an understanding of the gaps in care emerge, enabling them to be addressed early, and minimizing or even avoiding complications. This information supports new ways to engage patients with reminders; coaching and educational resources will increase the effectiveness and efficiency of the population health program as well.
3. Identifying areas for improvement through quality and efficiency reporting. Organizations also apply these analytics to measure quality and efficiency against established benchmarks providing reports to measure and report outcomes, and to identify areas of improvement opportunity. This insight can be used to understand a model’s effectiveness, assess provider efficiency and foster better payment modeling.
Examining needs and assessing capabilities.
Before making an investment in these critical resources, organizations will want to carefully examine their clinical and financial objectives. Since many entities will be pursuing an ACO model in stages, technology requirements will likely evolve over time. In particular, there are a few key features that should be examined to identify a scalable, cost-effective solution.
Real-time data processing
Using outdated data — weeks or even days old — to make clinical decisions is not a viable strategy in value-based care. For example, the first few days after a hospital discharge are a critical period for preventing readmission. Providers and care team members must be notified quickly in the event of a health status change. Systems that offer real-time data updates will ensure that providers are offering the most effective clinical response.
Flexibility and ease of use
Physicians, care managers, nurses and other staff all interact with patient data in unique ways. New technologies need to consider all of these distinct users. Solutions should feature quick view, configurable dashboards that put the most essential data in easy reach. Security features can also ensure that users have access to only the data required for their functional role.
While the data provided at the point of care is mission-critical, reporting features should not be overlooked. To support continuous quality improvement, organizations need flexible reporting at a population, practice and provider level. Systems should also integrate financial, clinical and operations data to provide a true perspective of the model’s effectiveness.
Since many health care entities are already far down the path of implementing electronic medical records (EMR), new technology must enhance these existing efforts. Organizations should consider compatibility with a current EMR or health exchange before implementing any new system. If a solution offers seamless integration, it will reduce implementation hassles and require minimal ongoing support.
Pairing technology with innovations in care delivery
Just as the health care industry is quickly evolving toward value-based care, new technologies are also keeping pace with these changes. Health IT has progressed far beyond basic EMR into solutions that enhance provider workflow and provide advanced clinical insight. These systems can empower providers to make better clinical decisions and improve care coordination. At the same time, new e-technologies can encourage patients to take an active role in health improvement.
By leveraging these resources to improve quality across the care spectrum, organizations will be well-positioned to succeed in the world of value-based care.