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:

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]. 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].

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].

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.

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 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].

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.