DMMS: A Decentralized Blockchain Ledger for the Management of Medication Histories

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Abstract

Background

Access to accurate and complete medication histories across healthcare institutions enables effective patient care. Histories across healthcare institutions currently rely on centralized systems for sharing medication data. However, there is a lack of efficient mechanisms to ensure that medication histories transferred from one institution to another are accurate, secure, and trustworthy.

Methods

In this article, we introduce a decentralized medication management system (DMMS) that leverages the advantages of blockchain to manage medication histories. DMMS is realized as a decentralized network under the hyperledger fabric framework. Based on the network, we designed an architecture, within which each prescriber can create prescriptions for each patient and perform queries about historical prescriptions accordingly. Finally, we analyzed the advantages of DMMS over centralized systems in terms of accuracy, security, trustworthiness, and privacy.

Results

We developed a proof of concept to showcase DMMS. In this system, a prescriber prescribes medications for a patient and then encrypts the prescriptions via the patient’s public keys. Patients can query their own prescriptions from different histories across healthcare institutions and then decrypt the prescriptions via their private keys. At the same time, a prescriber can query a patient’s prescription records across healthcare institutions after approval from the patient. Analytic results show that DMMS can improve security, trustworthiness, and privacy in medication history sharing and exchanging across healthcare institutions. In addition, we discuss the potential for DMMS in e-prescribing markets.

Conclusions

This study shows that a distributed secure ledger can enable reliable, interoperable, and accurate medication history sharing.

Related collections

Most cited references 25

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Hyperledger fabric

Elli Androulaki, Yacov Manevich, Srinivasan Muralidharan … (2018)

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Relationship between medication errors and adverse drug events.

David Bates, Deborah L Boyle, Martha Vliet … (1995)

To evaluate the frequency of medication errors using a multidisciplinary approach, to classify these errors by type, and to determine how often medication errors are associated with adverse drug events (ADEs) and potential ADEs. Medication errors were detected using self-report by pharmacists, nurse review of all patient charts, and review of all medication sheets. Incidents that were thought to represent ADEs or potential ADEs were identified through spontaneous reporting from nursing or pharmacy personnel, solicited reporting from nurses, and daily chart review by the study nurse. Incidents were subsequently classified by two independent reviewers as ADEs or potential ADEs. Three medical units at an urban tertiary care hospital. A cohort of 379 consecutive admissions during a 51-day period (1,704 patient-days). None. Over the study period, 10,070 medication orders were written, and 530 medications errors were identified (5.3 errors/100 orders), for a mean of 0.3 medication errors per patient-day, or 1.4 per admission. Of the medication errors, 53% involved at least one missing dose of a medication; 15% involved other dose errors, 8% frequency errors, and 5% route errors. During the same period, 25 ADEs and 35 potential ADEs were found. Of the 25 ADEs, five (20%) were associated with medication errors; all were judged preventable. Thus, five of 530 medication errors (0.9%) resulted in ADEs. Physician computer order entry could have prevented 84% of non-missing dose medication errors, 86% of potential ADEs, and 60% of preventable ADEs. Medication errors are common, although relatively few result in ADEs. However, those that do are preventable, many through physician computer order entry.

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Frequency, type and clinical importance of medication history errors at admission to hospital: a systematic review.

Romina Marchesano, S. Knowles, E Etchells … (2005)

Over a quarter of hospital prescribing errors are attributable to incomplete medication histories being obtained at the time of admission. We undertook a systematic review of studies describing the frequency, type and clinical importance of medication history errors at hospital admission. We searched MEDLINE, EMBASE and CINAHL for articles published from 1966 through April 2005 and bibliographies of papers subsequently retrieved from the search. We reviewed all published studies with quantitative results that compared prescription medication histories obtained by physicians at the time of hospital admission with comprehensive medication histories. Three reviewers independently abstracted data on methodologic features and results. We identified 22 studies involving a total of 3755 patients (range 33-1053, median 104). Errors in prescription medication histories occurred in up to 67% of cases: 10%- 61% had at least 1 omission error (deletion of a drug used before admission), and 13%- 22% had at least 1 commission error (addition of a drug not used before admission); 60%- 67% had at least 1 omission or commission error. Only 5 studies (n = 545 patients) explicitly distinguished between unintentional discrepancies and intentional therapeutic changes through discussions with ordering physicians. These studies found that 27%- 54% of patients had at least 1 medication history error and that 19%- 75% of the discrepancies were unintentional. In 6 of the studies (n = 588 patients), the investigators estimated that 11%-59% of the medication history errors were clinically important. Medication history errors at the time of hospital admission are common and potentially clinically important. Improved physician training, accessible community pharmacy databases and closer teamwork between patients, physicians and pharmacists could reduce the frequency of these errors.

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Author and article information

Journal

Journal ID (publisher-id): BHTY

Title: Blockchain in Healthcare Today

Publisher: Partners in Digital Health

ISSN (Electronic): 2573-8240

Publication date (Electronic): 31 December 2018

Publication date (Print): 2018

Volume: 2

Electronic Location Identifier: 10.30953/bhty.v2.38

Affiliations

[1 ]Computer Science, Saratoga High School, Saratoga, CA, USA

[2 ]Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

[3 ]Department of Biostatistics, School of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

[4 ]Department of Electrical Engineering & Computer Science, School of Engineering, Vanderbilt University, Nashville, TN, USA

Author notes

Corresponding Author: You Chen, PhD, Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Ave, Suite 1475, Nashville, TN 37203, USA. Email: you.chen@ 123456vumc.org

Article

Publisher ID: 38

DOI: 10.30953/bhty.v2.38

PMC ID: 7286573

PubMed ID: 32524086

SO-VID: bbd3f656-8664-4969-8a1d-59cef97a80f6

License:

This is an open access article distributed in accordance with the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) license, which permits others to distribute, adapt, enhance this work noncommercially, and license their derivative works on different terms, provided the original work is properly cited as first published in Blockchain in Healthcare Today™, and the use is non-commercial.

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