Table 2:

Options to improve the value of routinely collected health data

Process	Options	Resources needed
Selecting priorities	Systematic review of all available evidence and description of potential research consequences Consideration of novelty, incremental value and usefulness of research in context of systematic literature review Focus on health care questions that have not been addressed and are difficult or impossible to address with other designs	Some funding resources are needed to conduct systematic reviews, but the pay back should be much greater because efficiency is improved, important questions are addressed and uninformative and redundant research is avoided.
Protocols and prespecification	Clear statement on which analyses are exploratory (post hoc) and which are the main study analyses planned a priori For planned nonexploratory research: Prespecified hypotheses, research questions, definitions, detailed statistical analysis plans, model assumptions Predetermination of effect sizes that are of clinical significance Falsification end points Validation (split or multiple datasets) Decision rules for the consequences of RCD–research findings	Best practices may be promoted by funders (as requirements for funding) and endorsed by journals and research communities
Registration	Registration of datasets to improve research agenda and to support data sharing and validation activities For planned nonexploratory research: Registration of protocols and planned analyses to reduce selective reporting bias	Some resources are needed to establish and maintain suitable registries; existing registries for clinical trials also include observational studies but may need to be modified for maximal relevance; registration should be informative and nonbureaucratic
Reporting	Transparent and complete reporting Results reported and interpreted in context of all evidence derived from systematic literature review	Journals, peer reviewers, funders and authorities (e.g., ethics committees) may consider requiring reporting guidelines for preparation of reports and manuscripts
Raw data availability	Consideration of ethical and privacy issues Pre-emptive planning on consent issues Address deidentification issues Promotion of sharing data and providing access Promotion of research networks and joint analyses to allow evaluation of internal and external validity	Preparation, cleaning, deposition, curation and meaningful sharing of datasets needs committed resources and standardization efforts
Research networks	Establishment of large research networks involving various stakeholders to consider various perspectives Harmonization/standardization of research conduct and data-sharing efforts (e.g., protocols for exchanging RCD, codes or datasets)	Resources are needed to build and maintain networks, such as OHSDI, but may lead to a multiplier effect on efficiency and major quality improvements
Research on research	Research on methods to synthesize evidence from various data sources Research on reliability of RCD studies Validation of methods used in RCD studies to minimize confounding by indication biases Validation of findings in validation datasets across datasets and/or compared with designs of other studies Better understanding of and tools to measure and improve risk of bias, data validity and generalizability	Resources needed to perform metaresearch projects

Note: OHDSI = Observational Health Data Sciences and Informatics program,26 RCD = routinely collected data.