We all know that population health is at the forefront of our industry right now. Improving care, reducing costs and increasing patient satisfaction is everyone's goal. But all of this is impossible without leveraging the right data. Historically, claims data has been more readily available and actionable. However, with today's new value-based models, claims data alone just isn't enough. Organizations now realize what they really need to improve outcomes: actionable information that can only come from the addition of clinical data.
Fueled by clinical data, analytics tools such as dashboards and report cards help doctors see-at both a population level and a patient level-where they stand in regard to outcomes per patient, by condition. For example, a doctor may find many elderly patients are being admitted to the emergency department (ED) with the flu. The doctor decides to reach out to patients over the age of 65 to promote getting the flu shot. Real-time clinical data coupled with claims data will better capture those patients for which there is a "gap" in care. By adding chronic conditions, such as diabetes, asthma, chronic heart failure and chronic obstructive pulmonary disease (COPD), to that dashboard, you can assess the impact on efficiency and in promoting better care across a broader population.
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In addition to promoting care throughout the continuum and across multiple providers, clinical analytics is also vital for improving care at the individual patient level. For example, when a patient with a heart-failure diagnosis is discharged from the hospital, clinical evidence helps providers identify a treatment plan for the patient. Clinical data, such as the use or absence of an angiotensin-converting enzyme (ACE) inhibitor, is needed to track compliance to the care plan. Stratified alerts can push information to entire care team at the right time. This care team may include a discharge planner, care manager, primary care physician (PCP), home care organization and more - to reduce the chances of complications. This in turn reduces ED visits and hospital readmissions, which are among the top cost drivers to manage in value-based care.
Support from Evolving IT infrastructure
Clinical analytics is highly dependent upon data liquidity, or the ability to share available patient data across health care systems at the right time. Electronic health records (EHRs) contain treasure troves of data that must be shared across the care continuum to make the greatest impact on improved care and lower costs.However, since not all providers are on the same EHR, they have limited data on their patients. Coupled with this challenge, the patient may be receiving care out-of-network, preventing a complete view of the patient. Claims data has been helpful in capturing the entire care of the patient, but its usefulness is hampered as access to the data is delayed.
Providers agree: The goal is to improve outcomes at a lower cost. To do so, they need solutions that help aggregate and organize EHR data, independent of an EHR system, in near real-time and leverage claims data to provide a complete view of care that was delivered. With these solutions, providers receive the following benefits:
- A more holistic view of each patient.
- Fewer gaps in care
- A lower incidence of medication errors.
- Ability to more efficiently manage multiple conditions throughout their populations.
Moving forward, organizations must begin focusing on making it easier for providers to administer care. This will be most successful when done within current workflows, rather than forcing workflows to change to accommodate the solution. They must also provide platform flexibility to address the needs of the multiple doctors.
Future of Clinical Analytics
The impact of sharing clinical data across the continuum will only grow as more organizations begin improving their population health efforts. Advancements will come to make it easier, for example, to understand which intervention techniques have the greatest impact for various socio-demographic groups. Text-based alerts might work best for a 42-year-old diabetic who is overdue for his hemoglobin A1C. Using a care manager might be the best way to engage a recently discharged Medicare patient who has limited transportation. With this insight, providers will be able to engage individuals and populations with the right platforms and media based on unique preferences. This will help improve operational efficiencies while optimizing resources.
Clinical analytics will also become more predictive, driven by the providers. Many of today's quality measures focus on patients already diagnosed for a condition. "Data lakes," which store data from multiple sources in raw form, will allow us to identify patients with increasing risk. They will help us understand the impact of early interventions with a lens on both cost and outcomes. Predictive analytics also helps a provider identify hidden risk by analyzing multiple data sources which otherwise might be incomplete or time-consuming to review.
The addition of patient-provided data will prove highly beneficial as well. The new class of health-enabled wearables and mobile-enabled programs are especially promising. These emerging applications allow patients to track weight, blood pressure, glucose levels, asthma inhaler use, and more. Providers may not be interested in reviewing all the data generated from such devices, but they are interested in the insights derived from analytic solutions that leverage this data. This is a potentially significant contributor to managing chronic conditions and improving population health.
Regardless of what new technologies and methodologies emerge, one thing is certain: Given the shift to value-based models and the ongoing evolution of health IT infrastructure, clinical analytics will continue to make a growing contribution to improving outcomes while lowering costs.
Cheryl Cruver is the vice president of provider client services at HDMS.
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