High Quality Reliable Data Drive Better Outcomes and Innovation in Medicine, Finds Editorial in Annals of NAMS

The editorial published in the Annals of the National Academy of Medical Sciences (India) has presented an overview of how data-driven research has transformed clinical medicine and public health over the last 75 years.

Modern medical science, built on research evidence and its continuous evolution, has significantly increased life expectancy. In India, life expectancy has risen from around 40 years at independence (1947) to over 67 years today, while globally it has grown from about 25 years in ancient Rome to more than 80 years in developed nations.

“This editorial is trying to emphasize the role of Data in medical research. Continued research in medicine has improved the length and quality of life. The research whether bench to bedside or vice-versa need a credible data to produce robust evidence after research analysis. The quality of publications over last 75 years show continued upward trends in level of evidence this indirectly reflects that our data recording is getting better because data is the backbone of research,” Dr. Anil K Jain, MS, MAMS, FAMS, FIOA, FRCS (Eng.), Editor-in-Chief, Annals of National Academy of Medical Sciences and listed among 2 percent global scientist by Stanford university.

To illustrate how improvements in data quality and recording systems have strengthened medical research, the author of the editorial, Dr. Anil K. Jain and colleague examined the evolution of orthopedic research that happened over the past 75 years (1950–2025).

During 1950 to 1970, most orthopedic publications were case reports, retrospective case series, and descriptions of surgical techniques, forming the backbone of orthopedic literature. The concept of evidence-based medicine (EBM) and the hierarchy of evidence had not yet evolved or been widely recognized.

Between 1970 and 1990, orthopedic research began shifting from descriptive and retrospective studies to more structured prospective and multicenter research. This period saw the emergence of EBM and the establishment of the hierarchy of evidence, with opinion and case reports at the lowest level (Level 5), retrospective case series as Level 4, case-control studies as Level 3, prospective case series as Level 2, and randomized controlled trials (RCTs) as Level 1. Meta-analyses of RCTs were considered the highest form of evidence, laying the foundation for modern, data-driven orthopedic research.

From 1990 to 2010, orthopedic research advanced rapidly with improved imaging, computer technology, multicenter RCTs, and registry-based studies. Reporting standards like CONSORT and STROBE were introduced, strengthening research quality. Global studies increasingly achieved higher levels of evidence (Level I–II), while Indian publications largely remained at Level IV–V and were mostly observational due to infrastructure and data constraints, though researchers in India started recognizing the need for robust data collection, analysis, and reporting.

In the recent period between 2010 and 2025, orthopedic research had entered a mature phase marked by more RCTs, registry-based studies, AI-assisted diagnostics, and translational research. Globally, research design, reporting standards, and statistical accuracy improved, while Indian research expanded in volume and recognition, addressing clinical issues relevant to limited-resource settings.

Reflecting on this evolution, Dr. Jain, noted, “If we look at the quality of Indian medical research, it was just a reporting of a case/case series or surgical techniques in 1950-70. However, later as data improved the research output has improved in subsequent decades and once computer data storage has been created, the content quality of medical research has improved over the best 15-20 years. Data recording of every patient can provide us with a sound basis of analysis to generate robust research evidence. The data is the fundamental of EBM. It allows us to understand disease patterns and guide towards better diagnostics and treatment.

He further explained, “The data is the basis of innovation and translational research, and is also a tool for safety monitoring and quality assurance for clinicians. The robust data at the institutional/national level helps in policy planning as well as forecasting trends and predicting treatment response. Artificial intelligence (AI) and machine learning rely on high-quality datasets to generate diagnostic and prognostic tools.”

On the role of data in improving public health planning and policy in India, Dr. Jain highlighted, “All, public/private sector institutions should record patient information while being treated longitudinally. The date generated will authenticate our research output, and the evidence will be robust on the clinical problem unique to our country. The continued research is the key to answering riddles on clinical problems unique to India. We see the natural history of disease, hence, solutions to clinical problems unique to us to be treated in our infrastructure will come from our research. This data at the state/national level will be helpful in policy planning, as well as to involve all algorithms for better public policy use. Even for AI and machine learning based algorithms, we need robust patient data.”