revise this paragraph to successively bring out the importance of "The

image.png

revise this paragraph to successively bring out the importance of "The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an open community data standard, designed to standardize the structure and content of observational data and to enable efficient analyses that can produce reliable evidence. A central component of the OMOP CDM is the OHDSI standardized vocabularies. The OHDSI vocabularies allow organization and standardization of medical terms to be used across the various clinical domains of the OMOP common data model and enable standardized analytics that leverage the knowledge base when constructing exposure and outcome phenotypes and other features within characterization, population-level effect estimation, and patient-level prediction studies" in this project and how its harmonisation will bring about improved antimicrobial stewardship. impact patients health and community change

analyse_media

I can read the paragraph text directly from your image in the chat. Here is the revised version that weaves in the significance of OMOP CDM and OHDSI vocabularies, and draws a clear line from data harmonisation to improved antimicrobial stewardship (AMS), patient health outcomes, and community-level change:

This project addresses these gaps by improving and harmonising existing AMS surveillance infrastructure across Zambia, building on established ICARS cohort structures, existing AMS committees, and ongoing PPS cycles. Central to this effort is the adoption of the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) - an open community data standard specifically designed to standardize the structure and content of observational data and to enable efficient, reproducible analyses that produce reliable evidence. By mapping Zambia's antibiotic prescribing data to the OMOP CDM drug exposure vocabulary, this project transforms fragmented, facility-specific records into a structured, interoperable format that can be queried and compared at scale.

A core driver of this standardisation is the OHDSI standardized vocabulary framework, which provides the terminological backbone for the OMOP CDM. The OHDSI vocabularies unify medical concepts - including drug names, indications, and clinical outcomes - across diverse clinical domains, enabling the construction of rigorous exposure and outcome phenotypes for characterization, population-level effect estimation, and patient-level prediction studies. In the context of antimicrobial stewardship, this means that antibiotic prescribing patterns, resistance profiles, and clinical outcomes can be defined consistently across all nine hospitals, regardless of how data were originally recorded. This vocabulary-driven standardisation directly enables the detection of AWaRe classification trends, the identification of high-risk prescribing behaviours, and the generation of AMS evidence that is methodologically sound and nationally comparable.

The downstream impact extends well beyond data management. When antibiotic use data are harmonised to a global standard, AMS teams gain the analytical power to identify overprescribing of Watch and Reserve category antibiotics, target interventions where resistance pressure is highest, and monitor the effect of stewardship programmes with confidence. At the patient level, this translates into more appropriate antibiotic selection, reduced exposure to broad-spectrum agents, lower rates of adverse drug events, and better clinical outcomes. At the community level, reduced inappropriate antibiotic use slows the emergence and spread of resistant organisms, protecting population health and preserving the efficacy of last-resort antibiotics for future generations. By integrating with the OHDSI Africa Chapter Medication Standardisation Working Group and aligning with the OMOP CDM, Zambia's PPS data will become interoperable with a global federated research network spanning over 100 countries - enabling cross-country AMR pharmacoepidemiology and drug utilisation research at continental and global scale [6, 7].

Key changes made:

Introduced OMOP CDM and OHDSI vocabularies as the technical solution to the fragmentation problem, not just as a footnote
Explained why the standard matters (phenotyping, analytics, reproducibility) in language tied to the AMS context
Drew an explicit causal chain: harmonisation → reliable AMS evidence → appropriate prescribing → better patient outcomes → community-level resistance reduction
Maintained the original references and factual claims while strengthening the narrative arc

image.png

include how this will improve :

Here is the revised paragraph with the economic and procurement dimensions woven in:

PPS datasets generated across the nine project hospitals reflect inconsistent drug naming conventions (mixing generic names, brand names, and free-text descriptions), varied metric indicators (ATC codes, RxNorm, or unmapped free text), and misaligned denominator reporting. This fragmentation prevents meaningful cross-facility comparison, undermines the ability to track AWaRe antibiotic utilisation trends, and limits the translation of PPS evidence into national AMR policy [4, 5]. The consequences are not only clinical but economic: without reliable, standardised prescribing data, hospitals cannot accurately identify unnecessary antibiotic expenditure, quantify the cost burden imposed by resistant infections, or make evidence-based procurement decisions. Antibiotics are purchased on the basis of historical habit rather than demonstrated need, and the financial and therapeutic cost of treatment failures driven by resistance goes unmeasured and unaddressed. A national cross-sectional survey confirmed that over 64% of healthcare professionals reported limited access to essential AMR data and inadequate IT infrastructure [4], and no hospital surveyed had an allocated AMS budget [5] - a finding that both reflects and reinforces this cycle of inefficient procurement and under-resourced stewardship.

A core driver of this standardisation is the OHDSI standardized vocabulary framework, which provides the terminological backbone for the OMOP CDM. The OHDSI vocabularies unify medical concepts - including drug names, indications, and clinical outcomes - across diverse clinical domains, enabling the construction of rigorous exposure and outcome phenotypes for characterization, population-level effect estimation, and patient-level prediction studies. In the AMS context, this means that antibiotic prescribing patterns, resistance profiles, and clinical outcomes can be defined consistently across all nine hospitals, regardless of how data were originally recorded. This vocabulary-driven standardisation directly enables the detection of AWaRe classification trends, the identification of high-risk prescribing behaviours, and the generation of AMS evidence that is methodologically sound and nationally comparable.

The downstream impact extends across clinical, economic, and community dimensions. When antibiotic use data are harmonised to a global standard, AMS teams gain the analytical power to pinpoint where unnecessary antibiotic expenditure is occurring - whether through overprescribing of broad-spectrum agents, duplication of therapy, or prolonged treatment courses that are not supported by clinical outcomes data. Standardised, facility-level prescribing data also make the true cost of resistant infections visible for the first time: longer hospital stays, escalation to Reserve-category antibiotics, and treatment failures can be linked to specific prescribing patterns and quantified in economic terms. This evidence base directly informs smarter, needs-driven procurement - enabling pharmacy and supply chain teams to align antibiotic stock with actual clinical demand, reduce wastage, and prioritise Access-category antibiotics in line with WHO AWaRe guidance, rather than defaulting to costlier Watch and Reserve agents.

At the patient level, this translates into more appropriate antibiotic selection, reduced exposure to unnecessary or high-risk agents, lower rates of adverse drug events, and better clinical outcomes. At the community level, reduced inappropriate antibiotic use slows the emergence and spread of resistant organisms, protecting population health and preserving the efficacy of last-resort antibiotics for future generations. By integrating with the OHDSI Africa Chapter Medication Standardisation Working Group and aligning with the OMOP CDM, Zambia's PPS data will become interoperable with a global federated research network spanning over 100 countries - enabling cross-country AMR pharmacoepidemiology and drug utilisation research at continental and global scale [6, 7].

What was added:

A dedicated sentence block in paragraph one connecting fragmented data to the three named problems: unnecessary antibiotic expenditure, costs from resistant infections, and inefficient procurement
In the final analytical paragraph, each of those three themes is given its own explanatory sentence showing exactly how OMOP/OHDSI harmonisation addresses it - from spotting overspend, to quantifying resistance costs, to enabling demand-driven procurement aligned with AWaRe targets

image.png

intergrate

Here is the revised and integrated community engagement section:

Community engagement and involvement is embedded as a foundational principle of this project, not an add-on activity. In line with NIHR's broad definition of community engagement, the communities most directly affected by AMR are understood to encompass not only hospital patients, prescribers, pharmacists, and AMS committee members at the nine facilities, but also patient representatives, civil society organisations, AMR advocacy groups, and hospital advisory committees. All of these groups are positioned as co-designers of the standardisation toolkit, not as passive research subjects. This expansive view of community reflects the reality that antimicrobial resistance is not a problem confined to hospital walls - its drivers and consequences are felt across households, workplaces, and health systems, and sustainable solutions require ownership at every level.

Frontline engagement will occur throughout all three phases: AMS committee members and ward pharmacists will participate in Phase 1 nomenclature mapping exercises; prescribers and data entry clerks will co-design the toolkit interface in Phase 2 to ensure it fits the practical reality of their working environment; and facility staff will lead quarterly PPS cycles in Phase 3 with research team support. In parallel, patient representatives and AMR advocacy groups will be consulted at key decision points to ensure that the standardisation framework reflects the lived experience of those most affected by resistant infections - including communities where delayed or failed treatment has direct economic and social consequences. This co-design principle is the primary safeguard against producing a framework that is technically rigorous on paper but fails at the point of frontline adoption.

A dedicated CEI Lead will be named on the research team to coordinate engagement activities, manage community feedback mechanisms, and ensure vulnerable and marginalised patient groups - including those admitted to general medical wards with limited health literacy - have a meaningful voice in shaping how data about their care is collected and used. Civil society organisations and hospital advisory committees will further serve as accountability structures, providing independent oversight of how the toolkit is implemented and how findings are communicated back to communities. By embedding this breadth of community engagement from the outset, the project ensures that the move toward OMOP CDM harmonisation is not experienced as a technical imposition from above, but as a shared commitment to better, fairer, and more transparent antimicrobial stewardship for all.

What was integrated:

NIHR's broader definition of community engagement is named explicitly and used to expand the stakeholder list beyond the original four groups
Patient representatives, civil society, AMR advocacy groups, and hospital advisory committees are each given a functional role in the project, not just listed
The third paragraph connects their involvement to accountability and to the broader OMOP/AMS harmonisation goal, tying the community section back to the project's central purpose

propose research team members, roles and FTE for such a project

Based on the project scope - a multi-site AMS surveillance harmonisation study across nine Zambian hospitals, using OMOP CDM/OHDSI vocabularies, with three phases (nomenclature mapping, toolkit co-design, PPS cycles) and a strong community engagement component - here is a proposed research team:

Proposed Research Team, Roles, and FTE

Senior Leadership

Role	Responsibilities	FTE
Principal Investigator (PI)	Overall scientific and operational leadership; accountability to funder; final sign-off on all outputs; liaison with NIHR, ICARS, and OHDSI Africa Chapter	0.30
Co-Investigator - Clinical AMS	Clinical oversight of AMS protocols across all nine hospitals; ensures AWaRe alignment; leads clinical interpretation of prescribing data	0.25
Co-Investigator - Health Informatics / OMOP	Technical lead for OMOP CDM mapping and OHDSI vocabulary alignment; oversees ETL pipeline design and validation; liaison with global OHDSI network	0.25
Co-Investigator - Health Economics	Leads analysis of unnecessary antibiotic expenditure, resistance cost burden, and procurement inefficiency; supports economic outputs	0.20

Core Research Team

Role	Responsibilities	FTE
Project Manager	Day-to-day coordination across nine sites; milestone tracking; ethics and regulatory compliance; funder reporting; team scheduling	1.00
Clinical Research Fellow / Postdoctoral Researcher	Leads Phase 1 nomenclature mapping exercises; supports Phase 3 PPS cycle implementation; contributes to manuscript writing	1.00
Data Engineer / ETL Specialist	Builds and maintains the ETL pipeline from raw PPS datasets to OMOP CDM format; data quality checks; RxNorm and ATC code mapping	1.00
Pharmacist Researcher (x2, one per hospital cluster)	Embedded at facility level; conducts PPS data collection; trains ward pharmacists; validates drug nomenclature in real clinical context	1.00 each (2.00 total)
Biostatistician / Data Analyst	Designs analytical framework; conducts OHDSI-based characterisation studies; population-level effect estimation; patient-level prediction modelling	1.00
Community Engagement and Involvement (CEI) Lead	Coordinates all community engagement activities; manages feedback mechanisms; liaises with patient representatives, civil society, and AMR advocacy groups; ensures marginalised groups are represented	1.00

Site-Level Support (across nine hospitals)

Role	Responsibilities	FTE
Site Coordinator (x9, one per hospital)	Local data entry oversight; liaison between research team and hospital AMS committee; supports quarterly PPS cycles; ensures data completeness and accuracy	0.50 each (4.50 total)
Data Entry Clerk (x9, one per hospital)	Structured data entry into standardised toolkit; flags nomenclature inconsistencies; supports Phase 2 toolkit interface co-design	0.50 each (4.50 total)

Specialist and Advisory Roles

Role	Responsibilities	FTE
Infectious Disease / Microbiology Consultant	Advisory input on resistance pattern interpretation; links prescribing data to microbiological outcomes; supports clinical phenotyping	0.10
Health Literacy / Patient Advocacy Specialist	Ensures toolkit communications and consent materials are accessible to patients with limited health literacy; supports CEI Lead	0.20
IT / Systems Administrator	Maintains secure data infrastructure; ensures OMOP CDM database integrity; manages user access across sites	0.50

Summary Table

Category	Total FTE
Senior Leadership	1.00
Core Research Team	7.00
Site-Level Support (x9 sites)	9.00
Specialist and Advisory Roles	0.80
Total	17.80

Notes on FTE Allocation

Site Coordinators and Data Entry Clerks are proposed at 0.50 FTE to reflect that both roles will be filled by existing hospital staff (ward pharmacists, AMS committee members) with protected research time, consistent with the project's principle of building on existing infrastructure rather than creating parallel systems.
The CEI Lead is proposed at 1.00 FTE given the NIHR emphasis on broad community engagement spanning patient representatives, civil society, AMR advocacy groups, and hospital advisory committees - this is a substantive coordination role, not a part-time add-on.
The Data Engineer is proposed at 1.00 FTE for the full project duration because OMOP CDM ETL pipelines require continuous maintenance, re-mapping as vocabularies update, and quality assurance across all nine facility data feeds.
FTE figures assume a standard three-year project duration. Phase-specific intensity (e.g., the Data Engineer and Pharmacist Researchers being most active in Phases 1-2, the Biostatistician in Phase 3) should be reflected in the budget justification narrative rather than by reducing overall FTE, to maintain continuity and institutional knowledge.

grats manager?

I think you mean grants manager - a fair point. This role is often overlooked in research team proposals but is important, especially for an NIHR-funded multi-site project with complex subcontracting across nine hospitals in Zambia.

Here is the addition:

Grants and Finance Management

Role	Responsibilities	FTE
Grants Manager / Research Finance Officer	Manages funder financial reporting and compliance; processes subcontracts and payments to nine hospital sites; tracks budget expenditure against milestones; liaises with institutional finance office and NIHR grants team; supports audit preparation and end-of-grant financial returns	0.50

Why 0.50 FTE and not less: A nine-site project with site-level subcontracts, per-diems, equipment procurement, and international partner transactions generates substantial financial administration. At 0.20 FTE (a common underestimate), this role becomes a bottleneck that delays site payments and risks funder non-compliance. 0.50 FTE is realistic for the active phases and can taper in the final write-up year if the funder requires justification for reduction.

Where they sit in the team: The Grants Manager works directly alongside the Project Manager - the Project Manager owns timelines and deliverables, the Grants Manager owns the money. Separating these functions protects both roles and reduces the risk of a single point of failure in project administration.

Updated overall FTE total: 18.30

This is a shared conversation. Sign in to Orris to start your own chat.