Table 1.

Characteristics	Traditional, No. (%)^a (n = 2,171)	Telepharmacy, No. (%)^a (n = 661)	P Value
Patient sex
Female	1,100 (50.7)	336 (50.8)	.94^b
Male	1,071 (49.3)	325 (49.2)	.94^b
Age group, y
18–49	182 (8.4)	57 (8.6)	.10^b
50–64	509 (23.4)	134 (20.3)
65–74	750 (34.5)	261 (39.5)
>74	730 (33.6)	209 (31.6)
Patient location
Urban	1,568 (72.2)	105 (15.9)	<.001^b
Rural	603 (27.8)	556 (84.1)	<.001^b
Patient risk^d
High	555 (25.6)	142 (21.5)	.002^b
Moderate	816 (37.6)	225 (34.0)
Low	800 (36.8)	294 (44.5)
Payer
Medicaid	66 (3.0)	28 (4.2)	.13^b
Other	2,105 (97.0)	633 (95.8)	.13^b
Patient age, mean (SD), y	68.5 (13.1)	68.2 (12.9)	.63^c
No. of medications, mean (SD)^e	6.3 (4.5)	5.5 (4.2)	<.001^c

Table 1. Comparison of Overall Patient Characteristics by Pharmacy type to Evaluate Quality of Medication Use, 2013–2019

^a Percentages may not add to 100 because of rounding.

^b Derived from χ² test.

^c Derived from Student t test.

^d Tercile-based stratification of the medication counts for measure-eligible patients; varies for each quality measure.

^e Number of medications calculated as the count of distinct classes of dispensed medications.

Table 2.

Characteristics	Adherence to Noninsulin Diabetes Medications (n = 257)	Adherence to Renin-Angiotensin System Antagonist (n = 967)	Adherence to Statins (n = 1,034)	Use of High-Risk Medications^b (n = 1,985)	Statin Use In Persons With Diabetes (n = 159)
Patient sex
Female	125 (48.6)	436 (45.1)	482 (46.6)	1,104 (55.6)	73 (45.9)
Male	132 (51.4)	531 (54.9)	552 (53.4)	881 (44.4)	86 (54.1)
Age, y
18-49	38 (14.8)	151 (15.6)	108 (10.4)	—	22 (13.8)
50-64	116 (45.1)	365 (37.7)	432 (41.8)	—	73 (45.9)
65-74	50 (19.5)	239 (24.7)	255 (24.7)	1,046 (52.7)	59 (37.1)
>74	53 (20.6)	212 (21.9)	239 (23.1)	939 (47.3)	5 (3.1)
Patient location
Urban	168 (65.4)	603 (62.4)	698 (67.5)	1,127 (56.8)	79 (49.7)
Rural	89 (34.6)	364 (37.6)	336 (32.5)	858 (43.2)	80 (50.3)
Patient risk^c
Low	73 (28.4)	298 (30.8)	305 (29.5)	574 (28.9)	48 (30.2)
Moderate	93 (36.2)	341 (35.3)	378 (36.6)	711 (35.8)	54 (34.0)
High	91 (35.4)	328 (33.9)	351 (33.9)	700 (35.3)	57 (35.8)
Payer
Other	234 (91.1)	916 (94.7)	977 (94.5)	1,970 (99.2)	144 (90.6)
Medicaid	23 (8.9)	51 (5.3)	57 (5.5)	15 (0.8)	15 (9.4)
Pharmacy type
Traditional	202 (78.6)	753 (77.9)	852 (82.4)	1,510 (76.1)	114 (71.7)
Telepharmacy	55 (21.4)	214 (22.1)	182 (17.6)	475 (23.9)	45 (28.3)
Pharmacy pairs
Pair 3	35 (13.6)	169 (17.5)	169 (16.3)	412 (20.8)	38 (23.9)
Pair 2	90 (35.0)	335 (34.6)	307 (29.7)	804 (40.5)	121 (76.1)
Pair 1	132 (51.4)	463 (47.9)	558 (54.0)	769 (38.7)	—
Prevalence^d of Adherence or Inappropriate Use	188 (73.2)	731 (75.6)	755 (73.0)	164 (8.3)	105 (66.0)
Age, mean (SD) [IQR], y	62.3 (14.1) [53-73]	63.4 (13.7) [54-73]	64.5 (12.8) [55-74]	75.2 (7.9) [69-81]	60.9 (9.6) [54-69]
No. of medications, mean (SD) [IQR]^e	9.3 (4.1) [6-12]	8.2 (4.5) [5-11]	8.3 (4.5) [5-11]	5.6 (4.3) [2-8]	9.4 (4.4) [6-12]

Table 2. Description of Patient (N = 2,832) Characteristics by Outcomes for Medication Adherence and Inappropriate Use^a, 2013–2019

Abbreviations: — , not applicable; IQR, interquartile range.

^a All values are number (percentage) unless otherwise indicated.

^b Use of high-risk medications applies only to patients aged 65 or older, as per measure specifications.

^c Tercile-based stratification of the medication count for measure-eligible patients; varies for each quality measure.

^d Prevalence defined as all observations that met numerator specifications for each quality measure.

^e Number of medications calculated as the count of distinct therapeutic classes of dispensed medications.

Table 3.

Variables	Quality Measures
Variables	Adherence to Noninsulin Diabetes Medications	Adherence to Renin-Angiotensin System Antagonist Medications	Adherence to Statins	Use of High-Risk Medications^b (≥65 y)	Statin Use in Persons with Diabetes^c
Unadjusted model pharmacy type^a
Traditional	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Telepharmacy	0.6 (0.4-0.8) [.001]	1.1 (0.8-1.4) [.60]	1.0 (0.7-1.7) [.84]	0.9 (0.8-1.1) [.20]	0.9 (0.7-1.3) [.80]
Covariate adjusted model pharmacy type^a
Traditional	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Telepharmacy	0.8 (0.5-1.3) [.42]	1.0 (0.9-1.2) [.70]	1.3 (0.8-2.1) [.30]	1.3 (1.0-1.8) [.06]	1.7 (1.3-2.0) [<.001]
Patient sex
Male	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Female	1.4 (0.9-2.1) [.15]	0.7 (0.6-1.0) [.02]	0.9 (0.8-1.1) [.02]	1.1 (0.8-1.5) [.71]	0.3 (0.2-0.5) [<.001]
Age group
18-49	1 [Reference]	1 [Reference]	1 [Reference]	-	-
50-64	3.2 (1.5-7.2) [.004]	1.8 (1.3-2.5) [.001]	2.1 (1.7-2.4) [<.001]	-	-
65-74	6.9 (2.5-16.5) [<.001]	2.5 (1.8-3.3) [<.001]	2.6 (2.2-3.2) [<.001]	1 [Reference]	-
≥65	-	-	-	-	3.9 (2.2-7.2) [<.001]
<65	-	-	-	-	1 [Reference]
>74	2.3 (1.4-3.7) [<.001]	2.2 (1.6-3.2) [.001]	2.2 (1.6-2.9) [<.001]	0.8 (0.6-1.0) [.03]	-
Patient location
Urban	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Rural	0.4 (0.3-0.6) [<.001]	1.3 (1.1-1.6) [.005]	0.7 (0.5-0.9) [<.003]	0.9 (0.6-1.2) [.40]	0.7 (0.5-1.1) [.11]
Patient risk^d
Low	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Moderate	0.8 (0.4-1.9) [.69]	1.1 (0.7-1.5).80	0.9 (0.6-1.3) [.50]	5.5 (2.9-10.4) [<.001]	1.2 (0.7-2.1) [.49]
High	1.3 (0.5-3.3) [.52]	0.9 (0.7-1.1) [.40]	1.3 (1.0-1.5) [.02]	19.7 (10.6-36.3) [<.001]	2.1 (1.6-2.8) [<.001]
Payer
Medicaid	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Other	3.9 (2.1-6.9) [<.001]	1.9 (1.1-3.1) [<.02]	2.1 (1.2-3.8) [.01]	1.0 (0.4-2.2) [.94]	0.4 (0.3-0.6) [<.001]
Pharmacy pair
Pair 3	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Pair 2	3.1 (1.7-5.6) [.001]	1.7 (1.5-1.9) [<.001]	1.3 (1.0-1.7) [.08]	1.1 (1.0-1.3) [.13]	0.8 (0.7-1.0) [.01]
Pair 1	1.6 (0.9-2.9) [.14]	1.3 (1.1-1.4) [<.001]	0.6 (0.5-0.7) [<.001]	1.3 (1.1-1.5) [.01]	-

Table 3. Unadjusted and Covariate-Adjusted Estimates of the Effect of Pharmacy type on Quality of Medication Use

Abbreviation: — , not applicable.

^a All values are odds ratio (95% CI) and [P value].

^b Use of high-risk medications applies only to patients aged 65 or older, as per measure specifications.

^c Age groups combined for model development; no assessment for pharmacy Pair 1.

^d Tercile-based stratification of the medication count for measure-eligible patients; varies for each quality measure.

Table 4.

Characteristics	Quality Measures
Characteristics	Noninsulin Diabetes Medications Adherence	Renin-Angiotensin System Antagonist Adherence	Statin Adherence	Use of High-Risk Medications^b	Statin Use in Persons with Diabetes^c
Patient sex
Male	0.54 (0.46-0.61)	0.73 (0.65-0.79)	0.68 (0.60-0.75)	0.05 (0.03-0.08)	0.69 (0.65-0.74)
Female	0.61 (0.54-0.86)	0.67 (0.61-0.72)	0.67 (0.58-0.74)	0.05 (0.03-0.08)	0.87 (0.81-0.91)
Age group
18-49	0.33 (0.21-0.49)	0.57 (0.50-0.63)	0.53 (0.43-0.62)	-	-
50-64	0.62 (0.56-0.68)	0.70 (0.63-0.77)	0.70 (0.61-0.77)	-	-
<65	-	-	-	-	0.66 (0.63-0.69)
65-74	0.78 (0.68-0.85)	0.76 (0.71-0.81)	0.75 (0.67-0.81)	0.05 (0.03-0.09)	-
≥65	-	-	-	-	0.88 (0.82-0.93)
>74	0.54 (0.49-0.58)	0.74 (0.67-0.80)	0.71 (0.63-0.78)	0.04 (0.03-0.07)	-
Patient location
Urban	0.67 (0.60-0.73)	0.67 (0.58-0.74)	0.72 (0.63-0.79	0.05 (0.03-0.09)	0.82 (0.78-0.85)
Rural	0.47 (0.42-0.52)	0.73 (0.68-0.77)	0.63 (0.54-0.71)	0.04 (0.03-0.08)	0.77 (0.69-0.83)
Patient risk^d
Low	0.56 (0.41-0.71)	0.70 (0.65-0.75)	0.67 (0.56-0.76)	0.01 (0.00-0.02)	0.74 (0.67-0.80)
Moderate	0.52 (0.42-0.62)	0.71 (0.62-0.79)	0.64 (0.56-0.72)	0.06 (0.04-0.08)	0.77 (0.72-0.82)
High	0.63 (0.50-0.75)	0.68 (0.61-0.74)	0.72 (0.64-0.78)	0.17 (0.12-0.25)	0.85 (0.78-0.91)
Payer
Non-Medicaid	0.73 (0.68-0.77)	0.76 (0.75-0.77)	0.75 (0.72-0.78)	0.05 (0.04-0.06)	0.72 (0.70-0.74)
Medicaid	0.41 (0.30-0.52)	0.63 (0.50-0.74)	0.59 (0.43-0.73)	0.05 (0.02-0.12)	0.85 (0.79-0.90)
Pharmacy pair
Pair 3	0.44 (0.34-0.55)	0.64 (0.58-0.70)	0.70 (0.64-0.75)	0.04 (0.03-0.07)	0.81 (0.75-0.85)
Pair 2	0.71 (0.65-0.76)	0.75 (0.69-0.80)	0.75 (0.65-0.82)	0.05 (0.03-0.08)	0.78 (0.74-0.81)
Pair 1	0.56 (0.48-0.63)	0.69 (0.64-0.74)	0.57 (0.47-0.66)	0.05 (0.03-0.09)	-
Pharmacy type
Traditional	0.60 (0.52-0.67)	0.69 (0.65-0.73)	0.65 (0.59-0.70)	0.04 (0.03-0.07)	0.75 (0.69-0.80)
Telepharmacy	0.55 (0.47-0.63)	0.70 (0.63-0.77)	0.70 (0.58-0.80)	0.06 (0.03-0.10)	0.83 (0.79-0.87)

Table 4 Predicted Margins From Adjusted Models of Medication Adherence and Inappropriate Use Using Least Square Means^a

Abbreviations: — , not applicable.

^a All values are predicted margin (95% CI).

^b Use of high-risk medications applies only to patients aged 65 or older, as per measure specifications.

^c Age groups combined for model development; no assessment for Pharmacy Pair 1.

^d Tercile-based stratification of the medication count for measure-eligible patients; varies for each quality measure.

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CME / ABIM MOC

Telepharmacy and Quality of Medication Use in Rural Areas, 2013–2019

Authors: Shweta Pathak, MPH, PhD; Mitchell Haynes, PharmD; Dima M. Qato, PharmD, PhD, MPH; Benjamin Y. Urick, PharmD, PhD
CME / ABIM MOC Released: 9/3/2020
THIS ACTIVITY HAS EXPIRED
Valid for credit through: 9/3/2021

Start Activity

Target Audience and Goal Statement

This activity is intended for all physicians who prescribe medications.

The goal of this activity is to evaluate patient and pharmacy characteristics between telepharmacies and traditional pharmacies.

Upon completion of this activity, participants will be able to:

Distinguish remote services typically offered by telepharmacies
Compare characteristics of patients using traditional pharmacies and telepharmacies
Analyze medication adherence data in traditional pharmacies and telepharmacies
Evaluate outcomes improved in telepharmacies vs traditional pharmacies

Disclosures

As an organization accredited by the ACCME, Medscape, LLC, requires everyone who is in a position to control the content of an education activity to disclose all relevant financial relationships with any commercial interest. The ACCME defines "relevant financial relationships" as financial relationships in any amount, occurring within the past 12 months, including financial relationships of a spouse or life partner, that could create a conflict of interest.

Medscape, LLC, encourages Authors to identify investigational products or off-label uses of products regulated by the US Food and Drug Administration, at first mention and where appropriate in the content.

Faculty

Shweta Pathak, MPH, PhD

University of North Carolina Eshelman School of Pharmacy
Chapel Hill, North Carolina

Disclosures

Disclosure: Shweta Pathak, MPH, PhD, has disclosed the following relevant financial relationships:
Other: Received funding from Cardinal Health

Mitchell Haynes, PharmD

University of North Carolina Eshelman School of Pharmacy
Chapel Hill, North Carolina

Disclosures

Disclosure: Mitchell Haynes, PharmD, has disclosed no relevant financial relationships.

Dima M. Qato, PharmD, PhD, MPH

University of Illinois College of Pharmacy
Chicago, Illinois

Disclosures

Disclosure: Dima M. Qato, PharmD, PhD, MPH, has disclosed the following relevant financial relationships:
Other: Received funding from Cardinal Health

Benjamin Y. Urick, PharmD, PhD

University of North Carolina Eshelman School of Pharmacy
Chapel Hill, North Carolina

Disclosures

Disclosure: Benjamin Y. Urick, PharmD, PhD, has disclosed the following relevant financial relationships:
Received grants for clinical research from: Cardinal Health, which owns the telepharmacy software used by pharmacies in this project.

CME Author

Charles P. Vega, MD

Health Sciences Clinical Professor of Family Medicine
University of California, Irvine School of Medicine
Irvine, California

Disclosures

Disclosure: Charles P. Vega, MD, has disclosed the following relevant financial relationships:
Served as an advisor or consultant for: Johnson & Johnson Pharmaceutical Research & Development, L.L.C.; GlaxoSmithKline
Served as a speaker or a member of a speakers bureau for: Genentech; GlaxoSmithKline

Editor

Robin Sloan

Editor, Preventing Chronic Disease

Disclosures

Disclosure: Robin Sloan has disclosed no relevant financial relationships.

CME/Content Reviewer

Esther Nyarko, PharmD

Associate Director, Accreditation and Compliance Medscape, LLC

Disclosures

Disclosure: Esther Nyarko, PharmD, has disclosed no relevant financial relationships.

Stephanie Corder, ND, RN, CHCP

Associate Director, Accreditation and Compliance

Disclosures

Disclosure: Stephanie Corder, ND, RN, CHCP, has disclosed no relevant financial relationships.

Medscape, LLC staff have disclosed that they have no relevant financial relationships.

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From Preventing Chronic Disease

CME / ABIM MOC

Telepharmacy and Quality of Medication Use in Rural Areas, 2013–2019

Authors: Shweta Pathak, MPH, PhD; Mitchell Haynes, PharmD; Dima M. Qato, PharmD, PhD, MPH; Benjamin Y. Urick, PharmD, PhDFaculty and Disclosures

THIS ACTIVITY HAS EXPIRED

CME / ABIM MOC Released: 9/3/2020

Valid for credit through: 9/3/2021

processing....

Abstract and Introduction

Introduction

Pharmacy closures in rural areas is an increasingly common problem. Closures disrupt medication access and decrease adherence to prescription medications. Telepharmacy is a potential solution to this problem; however, research on the relationship between telepharmacy and the quality of medication use is scarce. Our study sought to address this gap by comparing the quality of telepharmacies serving rural areas and traditional pharmacies that support them.

Methods

We obtained dispensing data for the first 18 months of operation from 3 telepharmacies and 3 traditional pharmacies located in the upper Midwest. We evaluated adherence for noninsulin diabetes medications, renin-angiotensin system antagonists, and statins, as well as inappropriate use of high-risk medications in older adults and statin use in persons with diabetes. All metrics were calculated using Medicare Part D specifications. We estimated the differences between telepharmacies serving rural areas and traditional pharmacies using generalized linear regression. We adjusted our models for potential sociodemographic and clinical confounders.

Results

A total of 2,832 patients contributed 4,402 observations to the quality measures. After covariate adjustment, we observed no significant differences between telepharmacies and traditional pharmacies for noninsulin diabetes medications, renin-angiotensin system antagonists, statins, and high-risk medications. However, statin use in persons with diabetes was higher in telepharmacies than traditional pharmacies.

Conclusion

We found that the quality of medication use at telepharmacies that serve rural areas was no worse than at traditional pharmacies. For communities considering the adoption of telepharmacy, results indicate that telepharmacies provide a suitable solution for expanding medication access and that using telepharmacy would not negatively affect the quality of medication use.

Introduction

Across the United States, rural populations are decreasing and growing older ^[1]. As a result, local businesses close in many small rural towns, and pharmacies that dispense medications to older adults are at risk of closing ^[2]. In 2018, 16% of rural independent pharmacies had closed during the previous 16 years ^[3]. Community pharmacies dispense 90% of medications in the United States ^[4], and pharmacy closures create disruptions in medication access that negatively affect medication adherence ^[5]. Decreasing adherence rates lead to greater disease progression and create a substantial financial burden on the health care system ^[6].

A potential solution for maintaining medication access in rural communities is telepharmacy, which is the provision of patient care by pharmacists through the use of telecommunication or other technologies ^[7]. In the community setting, telepharmacy most often replaces a physical check of patient adherence by a pharmacist with a remote check by a pharmacist. Filling prescriptions by a pharmacy technician also occurs under remote supervision. Additionally, patient counseling services are delivered by telephone or by video connection, as needed ^[8].

Although regulatory restrictions on telepharmacy have eased in recent years, as of 2016, less than half of all US states had rules or legislation authorizing telepharmacy practice ^[9]. The safety of telepharmacy services has been explored to some extent ^[10,11], but the effects of telepharmacy on the quality of medication use is largely unknown. Limiting physical access to a pharmacist might negatively influence the quality of medication use, and this uncertainty has created barriers for the implementation of regulations that make telepharmacy licensure possible ^[12]. The primary objective of our study was to evaluate the relationship between telepharmacy services in rural areas and the quality of medication use.

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Methods

CME / ABIM MOC Information

Abstract and Introduction
Methods
Results
Discussion

Disclaimer

The educational activity presented above may involve simulated, case-based scenarios. The patients depicted in these scenarios are fictitious and no association with any actual patient, whether living or deceased, is intended or should be inferred. The material presented here does not necessarily reflect the views of Medscape, LLC, or any individuals or commercial entities that support companies that support educational programming on medscape.org. These materials may include discussion of therapeutic products that have not been approved by the US Food and Drug Administration, off-label uses of approved products, or data that were presented in abstract form. These data should be considered preliminary until published in a peer-reviewed journal. Readers should verify all information and data before treating patients or employing any therapies described in this or any educational activity. A qualified healthcare professional should be consulted before using any therapeutic product discussed herein.

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References

Faculty and Disclosures

Faculty

Shweta Pathak, MPH, PhD

Mitchell Haynes, PharmD

Dima M. Qato, PharmD, PhD, MPH

Benjamin Y. Urick, PharmD, PhD

CME Author

Charles P. Vega, MD

Editor

Robin Sloan

CME/Content Reviewer

Esther Nyarko, PharmD

Stephanie Corder, ND, RN, CHCP

CME / ABIM MOC

Telepharmacy and Quality of Medication Use in Rural Areas, 2013–2019

Target Audience and Goal Statement

Disclosures

Faculty

Shweta Pathak, MPH, PhD

Mitchell Haynes, PharmD

Dima M. Qato, PharmD, PhD, MPH

Benjamin Y. Urick, PharmD, PhD

CME Author

Charles P. Vega, MD

Editor

Robin Sloan

CME/Content Reviewer

Esther Nyarko, PharmD

Stephanie Corder, ND, RN, CHCP

Accreditation Statements

For Physicians

Instructions for Participation and Credit

Telepharmacy and Quality of Medication Use in Rural Areas, 2013–2019

Abstract and Introduction

Introduction

Methods

Results

Conclusion

Introduction

Table of Contents