Clinical effectiveness and cost-effectiveness of issuing longer versus shorter duration (3-month vs. 28-day) prescriptions in patients with chronic conditions: systematic review and economic modelling.

BACKGROUND: To reduce expenditure on, and wastage of, drugs, some commissioners have encouraged general practitioners to issue shorter prescriptions, typically 28 days in length; however, the evidence base for this recommendation is uncertain. OBJECTIVE: To evaluate the evidence of the clinical effectiveness and cost-effectiveness of shorter versus longer prescriptions for people with stable chronic conditions treated in primary care. DESIGN/DATA SOURCES: The design of the study comprised three elements. First, a systematic review comparing 28-day prescriptions with longer prescriptions in patients with chronic conditions treated in primary care, evaluating any relevant clinical outcomes, adherence to treatment, costs and cost-effectiveness. Databases searched included MEDLINE (PubMed), EMBASE, Cumulative Index to Nursing and Allied Health Literature, Web of Science and Cochrane Central Register of Controlled Trials. Searches were from database inception to October 2015 (updated search to June 2016 in PubMed). Second, a cost analysis of medication wastage associated with < 60-day and ≥ 60-day prescriptions for five patient cohorts over an 11-year period from the Clinical Practice Research Datalink. Third, a decision model adapting three existing models to predict costs and effects of differing adherence levels associated with 28-day versus 3-month prescriptions in three clinical scenarios. REVIEW METHODS: In the systematic review, from 15,257 unique citations, 54 full-text papers were reviewed and 16 studies were included, five of which were abstracts and one of which was an extended conference abstract. None was a randomised controlled trial: 11 were retrospective cohort studies, three were cross-sectional surveys and two were cost studies. No information on health outcomes was available. RESULTS: An exploratory meta-analysis based on six retrospective cohort studies suggested that lower adherence was associated with 28-day prescriptions (standardised mean difference -0.45, 95% confidence interval -0.65 to -0.26). The cost analysis showed that a statistically significant increase in medication waste was associated with longer prescription lengths. However, when accounting for dispensing fees and prescriber time, longer prescriptions were found to be cost saving compared with shorter prescriptions. Prescriber time was the largest component of the calculated cost savings to the NHS. The decision modelling suggested that, in all three clinical scenarios, longer prescription lengths were associated with lower costs and higher quality-adjusted life-years. LIMITATIONS: The available evidence was found to be at a moderate to serious risk of bias. All of the studies were conducted in the USA, which was a cause for concern in terms of generalisability to the UK. No evidence of the direct impact of prescription length on health outcomes was found. The cost study could investigate prescriptions issued only; it could not assess patient adherence to those prescriptions. Additionally, the cost study was based on products issued only and did not account for underlying patient diagnoses. A lack of good-quality evidence affected our decision modelling strategy. CONCLUSIONS: Although the quality of the evidence was poor, this study found that longer prescriptions may be less costly overall, and may be associated with better adherence than 28-day prescriptions in patients with chronic conditions being treated in primary care. FUTURE WORK: There is a need to more reliably evaluate the impact of differing prescription lengths on adherence, on patient health outcomes and on total costs to the NHS. The priority should be to identify patients with particular conditions or characteristics who should receive shorter or longer prescriptions. To determine the need for any further research, an expected value of perfect information analysis should be performed. STUDY REGISTRATION: This study is registered as PROSPERO CRD42015027042. FUNDING: The National Institute for Health Research Health Technology Assessment programme

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

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