Standardizing Patient Outcome Measures

Outcome measure or more specifically put, Patient-Reported Outcomes can be defined as any report of the status of a patient’s health condition that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else. Outcome measure is one of those medical terms that keeps used all the time without any proper understanding of what it truly means. In addition to the definition given above outcome measure means a lot more and have different definitions. Properly understanding outcome measures is very important because it plays a huge role in the healthcare industry and that’s why it needs to be constantly standardized. Standardizing Patient Outcome Measures is important and crucial and to do this we must first fully understand what outcome measure are in their truest definitions and understand their principles of application and measurement.

Different Definition Of Outcomes:

  • are led by an experienced clinician (not necessarily a physician) who has a deep knowledge of the medical conditions and who is a true advocate for outcome measurement
  • are supported by a project leader from a quality management department consisting of dedicated people from different professional groups, across specialties, including outcome experts meeting regularly to define and improve outcome measures, risk adjustment factors and validated instruments
  • involve patients and their perspective into their indicator sets
  • meet and compare with peers on national and international level

Principles of Outcome Measurement

Outcomes should be measured by medical condition or primary care patient segment – Not by procedure or intervention. Outcomes should reflect the full cycle of care for the condition. Outcomes are always multidimensional and should include the health results most relevant to patients. Measurement must include initial conditions/risk factors to allow for risk adjustment. Standardize outcome measures to enable comparison and learning.

The principle of Supply and Demand with Outcome Measures

Supply gives and demand takes. But is that always the case? Here, we discuss the basic principles of supply and demand and how they relate to the standardization of outcome measure. Supply and demand curves are graphical representations of the price of a good on the y-axis, and the quantity of a good along the x-axis. They are very basic and fundamental economic models used to predict optimal prices and market reactions to market-changing events like technology. Generally, technological advances affect the supply curve, but other aspects of technology can also cause the demand curve to shift.

Equilibrium Price Point:

The supply curve is always upward sloping left to right, with the demand curve downward sloping. The intersection of these curves indicates the equilibrium price point or the price at which a supplier can offer goods and a consumer is willing to purchase them. Technology changes cause shifts along the supply and demand curves, which effectively moves the equilibrium price point up or down.

Supply Shift:

The most prominent and sought-after effect of technological advancement is the ability to increase production. Advancements in production mean suppliers can produce more goods at a cheaper cost, thus pushing the supply curve outward from left to right on the x-axis. This effectively lowers the equilibrium price point, unless demand for the product increases, or shifts outward left to right, to meet the increased production levels at the current price point.

Measuring Patient Outcome Measures

A patient-reported outcome measure (PROM) is an instrument, scale, or single-item measure used to assess the PRO concept as perceived by the patient, obtained by directly asking the patient to self-report. PROMs include any method used to collect patient input, from diaries and event logs to one-item or multi-item multi-domain scales.

PROs and PROMs have been used in healthcare for decades, but primarily in research settings. The Medical Outcome Study in the 1970s measured the impact of care patterns on outcomes for patients with chronic medical conditions and depression. The study used a 116-item survey to assess the quality of life including physical, mental, and general health. It was a landmark study; adapted forms of the study’s survey have become gold standards in the field.

Since the 1970s, hundreds of PROMs have been developed across nearly the full breadth of medicine, but for the most part, their use has remained narrow. However, as healthcare consumers become more interested in understanding and acting on their own health data, and clinicians demand analytics to understand the health of their patient populations, the demand for PROMs has surged.

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