Table 1.

Differences in Processes and Outcomes Between Care Isolated to a Single Disease and Primary Care

Construct	Isolated Single Disease with Linear Mechanical Processes	Primary Care Nonlinear Adaptive Processes
Care process
Process complexity	Few variables to be measured and controlled Example: central line bundles	Numerous variables that make accurate measurement problematic Example: patients with multiple medications, comorbidities, and socioeconomic challenges
Process standardization	Standard processes use consistent raw materials Example: antibiotics administered just before the incision is made in elective surgeries	Variable processes with variable raw materials Example: a wide range of disease severities and treatment options for the same diagnosis: eg, migraine, chronic low back pain, and fibromyalgia
Process controls	Machines and unconscious patients are largely controlled by their human operators Example: procedure not started until the pre-surgical checklist is completed and chlorhexidine antiseptic has been applied	The patient “machine” is controlled by a milieu of forces, including caregiver biases, unique patient beliefs, socioeconomic status, and the external environment Example: medication nonadherence associated with poverty, which is not controllable by the physician or the health care team
Outcome goals
Goal clarity: multimorbidity	All team members and machines work toward one clear goal Example: titanium artificial hip placed in the appropriate position	There is no one right answer or goal, only an individualized understanding of risks and benefits where ideally the patients chooses the best answer for him or her Example: another round of chemotherapy for a patient with metastatic cancer vs hospice care
Goal clarity: unique patient priorities	Patients and caregivers agree on clear outcome Example: minimum days intubated on mechanical ventilation	Patients have different goals or priorities from their caregivers’ recommendations Example: a diabetic patient who does not want to start taking insulin to reduce her blood glucose because of concerns about the affordability of the medicine and a belief that insulin killed her aunt
Goal timing	Standard processes have fixed expectations of the timing of interventions Example: daily trials of endotracheal tube extubation	The timing and order of addressing patient concerns are highly variable Example: the primary care physician and patient may negotiate and agree that a vague symptom be given more time to evolve, with no testing or treatment ordered the first time the concern is mentioned
Inadequate summative quality scorecards
Poor risk-adjustment tools	Coexisting patient complexities rarely affect process metrics Example: postoperative thrombosis prophylaxis	Coexisting patient complexities often affect patient outcomes Example: any number of social determinant factors affect disease- and patient-oriented outcomes
Goal target number	Six Sigma-level outcomes Example: 0% infection rate or 100% vaccination uptake	Outcomes are dependent on a multitude of social and behavioral cofactors Example: much less than 100% of a population wants colon cancer screening no matter how strongly it is recommended and incentivized
Scorecard comprehensiveness	List of metrics for a physician represents most of the work performed Example: an overall rating for an orthopedist who only replaces hips and knees	Few simplistic quality measures capture only a tiny fraction of the work performed by a primary care physician. The alternative is a long, cumbersome list that is costly and burdensome to maintain and of questionable validity