Gary Donaldson1. 1. Department of Anesthesiology, Pain Research Center, University of Utah, Salt Lake City, UT, USA. Gary.Donaldson@hsc.utah.edu
Abstract
PURPOSE: Coherent clinical care depends on answering a basic question: is a patient getting worse, getting better, or staying about the same? This can prove surprisingly difficult to answer confidently. Patient-reported outcomes (PROs) could potentially help by providing quantifiable evidence. But quantifiable evidence is not necessarily good evidence, as this article details. METHOD: The fundamental mandate of measurement requires that errors in making an assessment be smaller than the distinctions to be measured. This mandate implies that numerical observations of patients may be poor measurements. RESULTS: Individual assessments require high measurement precision and reliability. Group-averaged comparisons cancel out measurement error, but individual PROs do not. Individual PROs generate numbers, to be sure, but the numbers may fall short of what we should demand of measurements. When typical errors of measurement are large, it is not possible to answer confidently even the modest question of whether a patient is getting worse or getting better. CONCLUSION: This article explains some theory behind the mandate of measurement, provides several examples based on clinical research, and suggests strategies to measure and monitor individual patient outcomes more precisely. These include more frequent low-burden assessments, more realistic confidence levels, and strengthened measurement that integrates population data.
PURPOSE: Coherent clinical care depends on answering a basic question: is a patient getting worse, getting better, or staying about the same? This can prove surprisingly difficult to answer confidently. Patient-reported outcomes (PROs) could potentially help by providing quantifiable evidence. But quantifiable evidence is not necessarily good evidence, as this article details. METHOD: The fundamental mandate of measurement requires that errors in making an assessment be smaller than the distinctions to be measured. This mandate implies that numerical observations of patients may be poor measurements. RESULTS: Individual assessments require high measurement precision and reliability. Group-averaged comparisons cancel out measurement error, but individual PROs do not. Individual PROs generate numbers, to be sure, but the numbers may fall short of what we should demand of measurements. When typical errors of measurement are large, it is not possible to answer confidently even the modest question of whether a patient is getting worse or getting better. CONCLUSION: This article explains some theory behind the mandate of measurement, provides several examples based on clinical research, and suggests strategies to measure and monitor individual patient outcomes more precisely. These include more frequent low-burden assessments, more realistic confidence levels, and strengthened measurement that integrates population data.
Authors: Gary W Donaldson; C Richard Chapman; Yoshi Nakamura; David H Bradshaw; Robert C Jacobson; Christopher N Chapman Journal: Pain Date: 2003-03 Impact factor: 6.961
Authors: Claire F Snyder; Amanda L Blackford; Julie R Brahmer; Michael A Carducci; Roberto Pili; Vered Stearns; Antonio C Wolff; Sydney M Dy; Albert W Wu Journal: Qual Life Res Date: 2010-03-26 Impact factor: 4.147
Authors: Roxanne E Jensen; Nan E Rothrock; Esi M DeWitt; Brennan Spiegel; Carole A Tucker; Heidi M Crane; Christopher B Forrest; Donald L Patrick; Rob Fredericksen; Lisa M Shulman; David Cella; Paul K Crane Journal: Med Care Date: 2015-02 Impact factor: 2.983
Authors: Carol M Moinpour; Amy K Darke; Gary W Donaldson; Duane Cespedes; Christine R Johnson; Patricia A Ganz; Donald L Patrick; John E Ware; Sally A Shumaker; Frank L Meyskens; Ian M Thompson Journal: J Natl Cancer Inst Date: 2012-09-12 Impact factor: 13.506
Authors: Claire F Snyder; Neil K Aaronson; Ali K Choucair; Thomas E Elliott; Joanne Greenhalgh; Michele Y Halyard; Rachel Hess; Deborah M Miller; Bryce B Reeve; Maria Santana Journal: Qual Life Res Date: 2011-11-03 Impact factor: 4.147
Authors: Carol M Moinpour; Gary W Donaldson; Kimberly M Davis; Arnold L Potosky; Roxanne E Jensen; Julie R Gralow; Anthony L Back; Jimmy J Hwang; Jihye Yoon; Debra L Bernard; Deena R Loeffler; Nan E Rothrock; Ron D Hays; Bryce B Reeve; Ashley Wilder Smith; Elizabeth A Hahn; David Cella Journal: Qual Life Res Date: 2016-07-28 Impact factor: 4.147