Understanding what matters most to patients in acute care in seven countries, using the flash mob study design

Abstract Background Truly patient-centred care needs to be aligned with what patients consider important, and is highly desirable in the first 24 h of an acute admission, as many decisions are made during this period. However, there is limited knowledge on what matters most to patients in this phase of their hospital stay. The objective of this study was to identify what mattered most to patients in acute care and to assess the patient perspective as to whether their treating doctors were aware of this. Methods This was a large-scale, qualitative, flash mob study, conducted simultaneously in sixty-six hospitals in seven countries, starting November 14th 2018, ending 50 h later. One thousand eight hundred fifty adults in the first 24 h of an acute medical admission were interviewed on what mattered most to them, why this mattered and whether they felt the treating doctor was aware of this. Results The most reported answers to “what matters most (and why)?” were ‘getting better or being in good health’ (why: to be with family/friends or pick-up life again), ‘getting home’ (why: more comfortable at home or to take care of someone) and ‘having a diagnosis’ (why: to feel less anxious or insecure). Of all patients, 51.9% felt the treating doctor did not know what mattered most to them. Conclusions The priorities for acutely admitted patients were ostensibly disease- and care-oriented and thus in line with the hospitals’ own priorities. However, answers to why these were important were diverse, more personal, and often related to psychological well-being and relations. A large group of patients felt their treating doctor did not know what mattered most to them. Explicitly asking patients what is important and why, could help healthcare professionals to get to know the person behind the patient, which is essential in delivering patient-centred care. Trial registration NTR (Netherlands Trial Register) NTR7538

Understanding what matters most to patients in acute care in seven countries, using the flash mob study design

Abstract Background Truly patient-centred care needs to be aligned with what patients consider important, and is highly desirable in the first 24 h of an acute admission, as many decisions are made during this period. However, there is limited knowledge on what matters most to patients in this phase of their hospital stay. The objective of this study was to identify what mattered most to patients in acute care and to assess the patient perspective as to whether their treating doctors were aware of this. Methods This was a large-scale, qualitative, flash mob study, conducted simultaneously in sixty-six hospitals in seven countries, starting November 14th 2018, ending 50 h later. One thousand eight hundred fifty adults in the first 24 h of an acute medical admission were interviewed on what mattered most to them, why this mattered and whether they felt the treating doctor was aware of this. Results The most reported answers to “what matters most (and why)?” were ‘getting better or being in good health’ (why: to be with family/friends or pick-up life again), ‘getting home’ (why: more comfortable at home or to take care of someone) and ‘having a diagnosis’ (why: to feel less anxious or insecure). Of all patients, 51.9% felt the treating doctor did not know what mattered most to them. Conclusions The priorities for acutely admitted patients were ostensibly disease- and care-oriented and thus in line with the hospitals’ own priorities. However, answers to why these were important were diverse, more personal, and often related to psychological well-being and relations. A large group of patients felt their treating doctor did not know what mattered most to them. Explicitly asking patients what is important and why, could help healthcare professionals to get to know the person behind the patient, which is essential in delivering patient-centred care. Trial registration NTR (Netherlands Trial Register) NTR7538

sj-pdf-1-dst-10.1177_19322968221085273 – Supplemental material for A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

Supplemental material, sj-pdf-1-dst-10.1177_19322968221085273 for A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings by David C. Klonoff, Jing Wang, David Rodbard, Michael A. Kohn, Chengdong Li, Dorian Liepmann, David Kerr, David Ahn, Anne L. Peters, Guillermo E. Umpierrez, Jane Jeffrie Seley, Nicole Y. Xu, Kevin T. Nguyen, Gregg Simonson, Michael S. D. Agus, Mohammed E. Al-Sofiani, Gustavo Armaiz-Pena, Timothy S. Bailey, Ananda Basu, Tadej Battelino, Sewagegn Yeshiwas Bekele, Pierre-Yves Benhamou, B. Wayne Bequette, Thomas Blevins, Marc D. Breton, Jessica R. Castle, James Geoffrey Chase, Kong Y. Chen, Pratik Choudhary, Mark A. Clements, Kelly L. Close, Curtiss B. Cook, Thomas Danne, Francis J. Doyle, Angela Drincic, Kathleen M. Dungan, Steven V. Edelman, Niels Ejskjaer, Juan C. Espinoza, G. Alexander Fleming, Gregory P. Forlenza, Guido Freckmann, Rodolfo J. Galindo, Ana Maria Gomez, Hanna A. Gutow, Lutz Heinemann, Irl B. Hirsch, Thanh D. Hoang, Roman Hovorka, Johan H. Jendle, Linong Ji, Shashank R. Joshi, Michael Joubert, Suneil K. Koliwad, Rayhan A. Lal, M. Cecilia Lansang, Wei-An (Andy) Lee, Lalantha Leelarathna, Lawrence A. Leiter, Marcus Lind, Michelle L. Litchman, Julia K. Mader, Katherine M. Mahoney, Boris Mankovsky, Umesh Masharani, Nestoras N. Mathioudakis, Alexander Mayorov, Jordan Messler, Joshua D. Miller, Viswanathan Mohan, James H. Nichols, Kirsten Nørgaard, David N. O’Neal, Francisco J. Pasquel, Athena Philis-Tsimikas, Thomas Pieber, Moshe Phillip, William H. Polonsky, Rodica Pop-Busui, Gerry Rayman, Eun-Jung Rhee, Steven J. Russell, Viral N. Shah, Jennifer L. Sherr, Koji Sode, Elias K. Spanakis, Deborah J. Wake, Kayo Waki, Amisha Wallia, Melissa E. Weinberg, Howard Wolpert, Eugene E. Wright, Mihail Zilbermint and Boris Kovatchev in Journal of Diabetes Science and Technolog

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

Background:A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.Methods:We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.Results:The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.Conclusion:The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments