Opioid use disorder and health service utilization among COVID-19 patients in the US: A nationwide cohort from the Cerner Real-World Data

IntroductionDigital medication health service (DMHS) platforms are increasingly used by pharmaceutical companies to provide direct medication health services through digital methods like apps, hotlines, and web services, etcetera. However, the implications of such platforms in supporting health technology assessment (HTA) are rarely discussed in the literature. This presentation sets out the opportunities for using the DMHS platforms of pharmaceutical companies as real-world data sources for HTA.MethodsA mixed-method qualitative study combining literature review and case study was conducted. Relevant literature was identified by searching the Web of Science and PubMed databases. A case study on current DMHS platforms in China was carried out using an inductive approach to identify the key elements emerging from these platforms.ResultsDMHS platforms of pharmaceutical companies can identify multiple attributes of medication information needs regarding medical products, including effectiveness, safety, and economic factors. The platforms can respond to different stakeholders, including patients and their carers, doctors, nurses, pharmacists, etcetera. As one kind of interactive process, DMHS platforms can provide further services, including patient education, consultation, and evaluation, follow-up visits, chronic disease management, promotion of the rational use of drugs, therapeutic drug monitoring, and adverse drug reaction surveillance and reporting.ConclusionsThe DMHS platforms of pharmaceutical companies provide a unique and valuable real-world data source for HTA. These types of self-reported outcomes have not gained enough attention in HTA. Collective efforts by HTA agencies and pharmaceutical companies are needed to set strategies for integrating DMHS platforms into HTA.

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COVID19 Real-World Data for the US and Lessons to Re-Open Business

10.1101/2020.06.02.20120618 ◽

2020 ◽

Author(s):

Pascal J. Goldschmidt-Clermont

Keyword(s):

Enterprise Resource Planning ◽

Real World ◽

Resource Planning ◽

The United States ◽

Lessons Learned ◽

Planning System ◽

Real World Data ◽

World Data ◽

The Us ◽

Contact Tracking

AbstractIn March of 2020, the COVID19 pandemic had expanded to the United States of America (US). Companies designated as “essential” for the US had to maintain productivity in spite of the growing threat created by the SARS-CoV-2 virus. With this report, we present the response of one such company, the Lennar Corporation, a major homebuilder in the US. Within days, Lennar had implemented a morning health check via its enterprise resource planning system, to identify associates (employees) who were sick, or not in their “usual state of health”. With this survey, Lennar was able to ensure that no one sick would show up to work, and instead, would self-quarantine at home. Furthermore, with thorough contact tracking, associates exposed to COVID19 patients (suspected or RT-PCR test-confirmed), were also asked to self-quarantine. This survey, in addition to other safety measures, such as an overhaul of the company with nearly 50% of the company working from home, prolific communication, and many more measures, Lennar was able to function safely for its associates and successfully as an enterprise. The data that we present here are “real world data” collected in the context of working throughout a dreadful pandemic, and the lessons learned could be helpful to other companies that are preparing to return to work.

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PCV65 Service Utilization, Predictors and Costs among High-Risk Patients with Cardiovascular Disease: Using Real World Data from the Australian Reach Registry

Value in Health ◽

10.1016/j.jval.2012.03.668 ◽

2012 ◽

Vol 15 (4) ◽

pp. A123-A124

Author(s):

Z. Ademi ◽

C. Reid ◽

D. Bhatt ◽

P.G. Steg ◽

D. Liew

Keyword(s):

Cardiovascular Disease ◽

High Risk ◽

Service Utilization ◽

Real World ◽

Real World Data ◽

World Data ◽

High Risk Patients ◽

Risk Patients

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IPD2.01 Disease progression in patients with CF treated with ivacaftor: analyses of real-world data from the US and UK CF Registries

Journal of Cystic Fibrosis ◽

10.1016/s1569-1993(18)30285-6 ◽

2018 ◽

Vol 17 ◽

pp. S53 ◽

Cited By ~ 4

Author(s):

N. Volkova ◽

J. Evans ◽

M. Higgins ◽

D. Campbell ◽

S. Tian ◽

...

Keyword(s):

Disease Progression ◽

Real World ◽

Real World Data ◽

World Data ◽

The Us

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Characterization of insufficient responders to anti-tumor necrosis factor therapies in patients with moderate to severe psoriasis: real-world data from the US Corrona Psoriasis Registry

Journal of Dermatological Treatment ◽

10.1080/09546634.2019.1656797 ◽

2019 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Abby S. Van Voorhees ◽

Marc A. Mason ◽

Leslie R. Harrold ◽

Ning Guo ◽

Adriana Guana ◽

...

Keyword(s):

Tumor Necrosis Factor ◽

Real World ◽

Tumor Necrosis ◽

Severe Psoriasis ◽

Real World Data ◽

World Data ◽

The Us ◽

Necrosis Factor

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Identifying Clinical Risk Factors for Opioid Use Disorder using a Distributed Algorithm to Combine Real-World Data from a Large Clinical Data Research Network

10.1101/2020.08.16.20167577 ◽

2020 ◽

Author(s):

Jiayi Tong ◽

Zhaoyi Chen ◽

Rui Duan ◽

Wei-Hsuan Lo-Ciganic ◽

Tianchen Lyu ◽

...

Keyword(s):

Risk Factors ◽

Clinical Data ◽

Real World ◽

Distributed Algorithm ◽

Opioid Use Disorder ◽

Research Network ◽

Patient Privacy ◽

Opioid Use ◽

Real World Data ◽

Level Data

Because they contain detailed individual-level data on various patient characteristics including their medical conditions and treatment histories, electronic health record (EHR) systems have been widely adopted as an efficient source for health research. Compared to data from a single health system, real-world data (RWD) from multiple clinical sites provide a larger and more generalizable population for accurate estimation, leading to better decision making for health care. However, due to concerns over protecting patient privacy, it is challenging to share individual patient-level data across sites in practice. To tackle this issue, many distributed algorithms have been developed to transfer summary-level statistics to derive accurate estimates. Nevertheless, many of these algorithms require multiple rounds of communication to exchange intermediate results across different sites. Among them, the One-shot Distributed Algorithm for Logistic regression (termed ODAL) was developed to reduce communication overhead while protecting patient privacy. In this paper, we applied the ODAL algorithm to RWD from a large clinical data research network-the OneFlorida Clinical Research Consortium and estimated the associations between risk factors and the diagnosis of opioid use disorder (OUD) among individuals who received at least one opioid prescription. The ODAL algorithm provided consistent findings of the associated risk factors and yielded better estimates than meta- analysis.

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