By Bruce Reiner, MD, RadSite’s Chief Medical Officer

“Quality is everyone’s responsibility.” W. Edwards Deming

When thinking about quality performance in healthcare, we often focus on providers and payers to optimize clinical and financial outcomes. Quality experts also commonly track and analyze health indicators at the population level.

Often lost in these approaches is the assessment of the individual patient, who arguably is the single most important factor in determining the success of a medical treatment or service. Simply put, optimizing health outcomes is inherently intertwined with individual behavior—and how patient-specific attributes can be leveraged to promote better evidence-based treatment pathways while reducing unnecessary medical costs.

Correlating Patient Engagement Levels with Better Health and Lower Cost
A growing body of evidence links patient activation levels to health and cost outcomes, with more actively engaged patients having lower costs (e.g. decreased hospitalizations, fewer ER visits) than their patient counterparts who were less actively engaged.[i]Patient engagement levels reflect the willingness and ability of patients to actively participate in their own healthcare delivery and management. This may include a number of health-related activities such as diet, obtaining preventative care, compliance with medications, exercise, education, and weight loss.

An additional component of patient engagement is active participation in clinical feedback from the attending providers, which can improve the timeliness and quality of disease treatment and prevention. If each individual patient takes more responsibility for their own life and disease management through active collaboration with clinical providers, outcomes will improve.

Establishing Patient-Centric Measures
Because patients are an extremely heterogeneous group, creating standardized metrics for quality analysis can be a challenging (but not insurmountable) endeavor. The first step is to identify those patient-specific characteristics and attributes which are associated with enhanced patient outcomes. The next step is to incorporate these factors into a systematic and quantifiable form of analysis, which is largely independent of disease and patient demographics. While certain patient characteristics such as socio-economic and educational status may have some association with outcomes, the intended goal of these metrics is to provide each individual patient with an opportunity to control their own healthcare destiny through a proactive commitment to quality improvement and personal accountability.

A few representative patient-specific quality metrics are listed below with the intention of providing patients with objective feedback, which can in turn be used for self-improvement and empowerment. These may prove to be particularly beneficial to patients with chronic illness, who are the most susceptible to changes in clinical care delivery, technology, and economics.  Patient-centric factors that will likely promote better outcomes are:

  1. Pharmacologic compliance
  2. Clinical feedback
  3. Dietary compliance
  4. Lifestyle regimen
  5. Adherence to preventative medicine recommendations
  6. Timeliness and attendance of scheduled tests and medical appointments
  7. Minimal drug and alcohol usage
  8. Health literacy and education.

A successful benchmarking system should also seek to provide incentives to highly compliant and actively engaged patients in accordance with these personal quality score metrics. Examples of incentives may include reduced insurance costs, improved access to clinical care providers of choice, and timely scheduling of medical tests and procedures. The ultimate goal is to empower patients and improve outcomes, while ensuring that all patients have the same opportunity for economic and clinical success.

Applying These Approaches to Imaging
While patients are often overlooked as active participants in image quality analysis, surprisingly they play an important role. Patient compliance is an important determinant of quality outcomes in medical imaging  as relating to pre-exam preparation (e.g., CT colonography), compliance with breathing instructions (e.g. cardiac CT or MRI), sharing of pertinent clinical history (e.g., protocol optimization), pharmacologic data (e.g. allergies related to contrast administration), and prior imaging data (e.g. mammography). When taking a holistic approach to quality assessment, all participants should share responsibility, including patients.

Final Thoughts
In the future RadSite hopes to expand its current image quality accreditation to incorporate data-driven quality metrics for the purpose of improving patient outcomes through enhanced education, creation of best practice guidelines, and context-specific performance analytics. By including patients in this collective process, we hope to provide quantitative accountability for all interested parties.

[i] J Greene et al. J Gen Intern Med 2012; 5: 520-526
N Begum at el. Diabetes Res Clin Pract 2011; 2: 260267
MJ Shively et al. J Cardiovasc Nurs 2013; 1: 20-34

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