Currently Viewing:
The American Journal of Managed Care October 2018
Putting the Pieces Together: EHR Communication and Diabetes Patient Outcomes
Marlon P. Mundt, PhD, and Larissa I. Zakletskaia, MA
Primary Care Physician Resource Use Changes Associated With Feedback Reports
Eva Chang, PhD, MPH; Diana S.M. Buist, PhD, MPH; Matt Handley, MD; Eric Johnson, MS; Sharon Fuller, BA; Roy Pardee, JD, MA; Gabrielle Gundersen, MPH; and Robert J. Reid, MD, PhD
From the Editorial Board: Bruce W. Sherman, MD
Bruce W. Sherman, MD
Recent Study on Site of Care Has Severe Limitations
Lucio N. Gordan, MD, and Debra Patt, MD
The Authors Respond and Stand Behind Their Findings
Yamini Kalidindi, MHA; Jeah Jung, PhD; and Roger Feldman, PhD
The Characteristics of Physician Practices Joining the Early ACOs: Looking Back to Look Forward
Stephen M. Shortell, PhD, MPH, MBA; Patricia P. Ramsay, MPH; Laurence C. Baker, PhD; Michael F. Pesko, PhD; and Lawrence P. Casalino, MD, PhD
Currently Reading
Nudging Physicians and Patients With Autopend Clinical Decision Support to Improve Diabetes Management
Laura Panattoni, PhD; Albert Chan, MD, MS; Yan Yang, PhD; Cliff Olson, MBA; and Ming Tai-Seale, PhD, MPH
Clinical Outcomes and Healthcare Use Associated With Optimal ESRD Starts
Peter W. Crooks, MD; Christopher O. Thomas, MD; Amy Compton-Phillips, MD; Wendy Leith, MS, MPH; Alvina Sundang, MBA; Yi Yvonne Zhou, PhD; and Linda Radler, MBA
Medicare Savings From Conservative Management of Low Back Pain
Alan M. Garber, MD, PhD; Tej D. Azad, BA; Anjali Dixit, MD; Monica Farid, BS; Edward Sung, BS, BSE; Daniel Vail, BA; and Jay Bhattacharya, MD, PhD
CMS HCC Risk Scores and Home Health Patient Experience Measures
Hsueh-Fen Chen, PhD; J. Mick Tilford, PhD; Fei Wan, PhD; and Robert Schuldt, MA
An Early Warning Tool for Predicting at Admission the Discharge Disposition of a Hospitalized Patient
Nicholas Ballester, PhD; Pratik J. Parikh, PhD; Michael Donlin, MSN, ACNP-BC, FHM; Elizabeth K. May, MS; and Steven R. Simon, MD, MPH
Gatekeeping and Patterns of Outpatient Care Post Healthcare Reform
Michael L. Barnett, MD, MS; Zirui Song, MD, PhD; Asaf Bitton, MD, MPH; Sherri Rose, PhD; and Bruce E. Landon, MD, MBA, MSc

Nudging Physicians and Patients With Autopend Clinical Decision Support to Improve Diabetes Management

Laura Panattoni, PhD; Albert Chan, MD, MS; Yan Yang, PhD; Cliff Olson, MBA; and Ming Tai-Seale, PhD, MPH
Incorporating an autopend functionality into clinical decision support improved glycated hemoglobin laboratory test completion by between 21.1% and 33.9% for reminder messages read within 57 days.
Data

EHR data were combined with HMT metadata. We used a structured text mining process to categorize patient HMT reminders according to autopend and usual content.

Measures

Patient HMT reminders. “Post autopend” indicated all HMT reminders sent after November 13, 2012. “Read reminder” recorded whether the patient clicked on the HMT reminder. “Time to reading” measured the number of days between when the HMT reminder was sent and when the patient clicked on it.

Time to laboratory test completion. This measured the number of days between when the HMT reminder was sent and laboratory test completion.

Statistical Analysis

We examined unadjusted differences in patient, provider, and HMT reminder characteristics for reminders sent in the preautopend and postautopend periods. P values were calculated based on the results of χ2 tests, t tests, and nonparametric equality of medians tests.

A Cox proportional hazard model13 was used to estimate the likelihood of laboratory test completion based on 3 HMT reminder characteristics: preautopend versus postautopend period, read versus unread, and time to reading. The model adjusted for patient’s sex, self-reported race/ethnicity, age, insurance type, and Charlson Comorbidity Index score,14 along with the sex and specialty of the patient’s PCP. We addressed missing data in the explanatory variables by including a category for unknown. To account for repeated HMT reminders within patients, we clustered standard errors at the patient level. We included quarter fixed effects to control for secular trends. Schoenfeld tests rejected the proportional hazards assumption, so we added time-varying covariates with a natural log of time specification.13 We used the STCOX and LINCOM procedures in Stata 13.1 (StataCorp LP; College Station, Texas) and reported estimates in hazard ratios (HRs).15

RESULTS

In the period before autopend, 6329 HMT reminders were sent to 5197 patients (Table 1). Patients who received reminders in the preautopend period had an average age of 59 years and were primarily male (58.7%) and mainly white (46.6%) or Asian (30.9%); almost half (48.5%) were insured by a preferred provider organization. In comparison, patients who received reminders in the postautopend period were more likely to have Medicare fee-for-service or unknown insurance (P <.001) and a female PCP in family medicine (P <.001).

In the preautopend period, all HMT reminders had usual content. Most reminders (85.2%) were read by the end of the study period (Table 1). However, the median time to reading was 3 days, and 75% of the reminders were read within 38 days. Most of the laboratory tests associated with the reminders (81.2%) were completed by the end of the study period. The median time to completion was 48 days, with 75% of the laboratory tests completed within 106 days.

In the postautopend period, 87.0% of the HMT reminders included autopend content, reflecting the proportion of autopended orders approved by the PCPs, whereas 13.0% included usual content, which resulted from rejected orders. HMT reminders in the postautopend period were read slightly sooner than those in the preautopend period (median time to reading, 1 day vs 3 days; P <.001) (Table 1). eAppendix D illustrates that in the first 2 months after a reminder was sent, reminders with either autopend or usual content sent in the postautopend period were slightly more likely to be read than reminders sent in the preautopend period. The median time to laboratory test completion was also 8 days shorter in the post­autopend period (40 days vs 48 days; P <.001) (Table 1; eAppendix E). eAppendix D illustrates that 2 months after the reminder was sent, read reminders with autopend content were associated with higher rates of laboratory test completion (59.5%) than read reminders with usual content in either the preautopend period (52.2%) or the postautopend period (42.2%; P <.001).

Next, we compared the adjusted effect of receiving an HMT reminder in the postautopend period for read and unread reminders (Table 2). Comparing reminders read on the same day they were sent (time to reading, 0 days), reminders sent in the postautopend period were associated with a 33.9% increase in the likelihood of laboratory test completion (HR, 1.339; P <.01). However, for reminders read 60 days after being sent, this increase in likelihood was lower, at 20.4% (P = .055). The improvement in the likelihood of an A1C laboratory test being completed in the postautopend period remained significant (HR, 1.211; P = .050) for reminders read up to 57 days after being sent, which included 68.4% of all reminders. For unread reminders, there was no statistically significant difference in A1C laboratory test completion among patients who were sent reminders in the postautopend period.

We also compared the adjusted effect of reading an HMT reminder in the preautopend and postautopend periods (Table 2). In the preautopend period, a read reminder was associated with a 76.7% increase in the likelihood of laboratory test completion (HR, 1.767; P <.001; time to reading, 0 days) compared with an unread reminder. However, in the postautopend period, a similar read reminder was associated with a 143.1% increase in likelihood of completion (HR, 2.431; P <.001; time to reading, 0 days).


 
Copyright AJMC 2006-2019 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up