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Rethinking Attribution when Evaluating the Quality of Physician Care

Key takeaways:

  • Physician attribution is an essential factor within a successful performance improvement initiative related to the measurement of clinician-centric performance.
  • Healthcare organizations can realize significant benefits by utilizing data to drive performance, but doing so requires insight into how clinicians and patients map to measurable outcomes.
  • By using data derived from the PINC AI™ technology platform to attribute physicians, opportunities for improvement can be more accurately targeted, resulting in improved patient satisfaction and outcomes.

A healthcare organization's quality, safety, efficiency and cost outcomes are often evaluated on the basis of physician attribution - an approach that determines which physician should be assigned to a particular patient. In many cases, these measures are used to benchmark a physician or physician group against a peer comparator. Many of these outcomes are further used within value-based payment and corporate incentive programs, and to attract referrals. Appropriate attribution creates a system of shared accountability that enables the overall care team to collaborate with executive leadership to drive improved performance.

The concept of attribution has been long-standing. However, given the multi-disciplinary nature of attribution that includes data, IT, administrative, financial and clinical processes, there is not just one industry-accepted method.

As health systems continue to grapple with shrinking margins, workforce issues and other challenges, it will be increasingly important for leaders to develop robust mechanisms for physician attribution that can serve as a catalyst for measurable performance improvement.

Three Commonly Used Physician Attribution Methods

Physician attribution methods are often centered around the larger clinical episode of the patient and typically rely on a “plurality rule” where the attributed physician is identified as having the highest cost or quantity of visits within the patient’s clinical episode. Such methods, however, lack utility when evaluating quality in the inpatient setting, as inpatient providers are rarely attributed using plurality-based attribution. While the topic of inpatient attribution has received less attention in the larger discourse of quality measurement, there are several methods that can be employed to make more informed attribution decisions.

Role-based Attribution

Role-based attribution relies on the hospital-coded physician role. Typically, attending physicians are attributed to patients whose care is largely medical in nature. In the case of surgical patients, the principal procedure surgeon is typically attributed as responsible physician. Role based attribution is convenient and interpretable and is often sufficient as a crude measure of attribution. There are of course limitations with role-based methods. Classification of a physician’s role is hospital specific, leading to a lack of standardization and comparability across hospitals. Most commonly, hospitals code hospitalists and internal medicine physicians as the attending physicians; however, there are hospitals that choose to assign specialists in the attending role if they are deemed most influential in the patient’s care. Furthermore, role-based methods neglect the breadth of clinical and demographic information available in the patient record that may be valuable in making a more informed attribution decision.

Physician of Greatest Duration (PGD)

An alternative method to identify the primary attributed physician in the inpatient setting is to identify the physician acting in the attending role for the greatest duration of time. Patients are often seen by multiple physicians acting in an attending capacity throughout their inpatient stay, making it difficult to determine the physicians most influential in a patient’s care. Identifying physicians with the greatest duration helps overcome an arbitrary attribution designation and can provide a better estimate of the physicians providing the most patient care during a patient’s stay.

Specialty Attribution

New methods have emerged that consider the broader set of physician and patient information to identify the larger care team involved in a patient’s inpatient care in addition to the attending or principal procedure surgeon. Research by Premier data scientists has shown that a specialty-based attribution can improve attribution by identifying physicians providing specialized care unique to the patient’s clinical condition who may not be captured through role- or PGD-based method. Approaching attribution from a care team perspective is important as specialists are commonly overlooked when attributing a single attributed physician. Specialty attribution can be used in conjunction with role- or PGD-based attribution for a more holistic evaluation of quality measurement.

Attribution is not only a post-facto issue; while a patient is still being actively cared for, particularly in an inpatient setting, knowing which physician is caring for a patient is critical to timely delivery of care and communication. Unfortunately, in many health systems, adequate mechanisms to track the individual managing patient care are lacking, as providers may change from day to day. This plays out daily as nurses, case managers and pharmacists may not know which physician to call, specialists not knowing who to contact about their recommendations, and family members not knowing where to direct questions. Accurate attribution is therefore a real-time clinical management issue as much as it is an outcome management and performance improvement issue.

Attribution and Data Drive Performance

Data is critical to engaging physicians who are trained as scientists and are used to using data to determine changes in individual patient care plans or trends in population-based care. When data has not been properly vetted and appears to identify faulty attribution, it becomes an easy target for discreditation, which then risks overall performance improvement initiatives. When the data can no longer be trusted, there builds a sense within the physicians that leadership is more intent in wielding the data as a weapon, to create winners and losers, and to identify “negative outliers” than to truly determine root causes of clinical performance issues and to determine ways to work together on solving issues.

On the other hand, there are countless examples of an active physician engagement culture built around robust data where physicians have confidence in the data and learning methods and can course correct as needed. If physicians find the data trustworthy, they can better appreciate performance variation, even when unfavorable, and are more willing to adjust their approach as necessary. Rigorous data quality standards and robust methods lead to the development of a culture that trusts in data as well as the power of collaboration and creation of systems.

If physicians distrust data, their organization cannot benefit from the collective participation and engagement that is necessary to improve outcomes systematically. It becomes difficult to create systems of accountability, identify and adhere to standards, and deliver the kinds of value-driven outcomes that payers and patients are demanding.

Real-world Application of PINC AI™ Analytics

Physician attribution is a true multi-disciplinary issue which can only be solved when the right resources are brought together. PINC AI™ technology can be utilized by healthcare organizations to ensure they are tackling this issue in a way that is meaningful to their care environment. By leveraging the power of PINC AI™ technology, healthcare organizations can resolve their internal data capture and standard work processes which ultimately leads to improved outcomes including care quality, reduced costs, increased value-based payments and an engaged clinical workforce.

Are you ready to understand and resolve your most challenging data attribution issues and become industry leaders in performance? Learn how PINC AI™ technology can help you unlock the value of your data.

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