Implementing Handwritten Text Recognition to Automate Clinical Trials

Clinical trials are the evaluation of medical interventions, including medicinal; surgical, and behavioral interventions. Such studies help researchers to determine if the medical devices and medicines being observed are efficient and whether their exploitation has any side effects. Clinical trials together with drug descriptions are complex, delicate processes, which require high accuracy. Partially automating these might not only save resources but improve overall productivity and facilitate scientific research. The data directly relevant to conducted clinical trials must be: Up-to-date Reliable Verifiable Traceable Manual processing of data (overviews, summaries, study reports) is significantly slowed down and inefficient. To ensure the data is up-to-date, reliable, and easily traceable, research institutions should introduce smart tools for simplified data processing, for example, solutions utilizing HTR (handwritten text recognition). AI-driven handwritten text recognition for optimized data management Although more and more healthcare facilities are digitizing patient records and other important documents, most historical medical records remain handwritten, which complicates various processes such as clinical trials. Processing information in form of notes and questionnaires requires significant resource allocation and carries great risks associated with information inaccuracy. Using optical character recognition might provide valuable benefits such as business automation. ML-based handwritten text recognition might optimize: Information gathering and structuring Statistical processing Up-to-date monitoring Data exchange between sponsor and investigator But how does this work exactly? Implementation and business benefits Data management and operability Clinical trials consist of four phases: Phase 1 – Human pharmacology (safety and right dosage) Phase 2 – Therapeutic exploratory (efficacy and side effects) Phase 3 – Therapeutic confirmatory (efficacy and adverse reactions) Phase 4 – FDA approval (evaluation of long-term risks and benefits) At each research stage, there might be challenges associated with data management: Large amounts of paper-based source protocols Information inaccuracy Poor accessibility Complicated monitoring Implementing optical character recognition ( ) might solve these challenges and optimize document operability. The gathered information can be easily digitalized and accessed at any desired moment, as well as exchanged through an internal portal. OCR The recognized raw data might include: Protocols Calendars Questionnaires Photographs Patient diaries Participant interviews Phone logs Hospital records Letters from referring physicians Medication logs Original radiological films Pharmacy dispensing records Data accompanying source documents Clinic and office charts: Nurses notes Lab reports Pathology reports Surgical reports Radiology reports Referring physician’s progress notes A solution utilizing OCR might help extract and process data from standard, manually filled medical forms. An example of a manually filled medical form can be seen below: To optimize document management, a case report form can be previously designed, printed, and then filled in. This process might provide for more accurate reports and significant time savings. Some examples of pre-designed medical forms can be seen below: Insight-driven decision-making at each trial stage Besides improved data management and operability as well as flexibility, there are other benefits to mention. Implementing optical character recognition might provide an accurate statistical overview and enhance thought-out decision-making. The solution might facilitate each stage of the clinical trial: The design of sheets and figures The incorporation of appendixes, which include: Protocols Investigator related information Patient listings, including demographics and baseline patient data Sample Case Report Forms Additional information Related publications Technical statistical documentation Technical statistical details The reports containing tests and calculations from more than one case study can be done way more accurately. To achieve high accuracy, gathered information has to be placed in a homogeneous database that contains historical data. Properly designed sheets and figures with included additional appendixes might provide insightful oversight. This way, research institutions might increase overall productivity and save valuable resources. Rounding up Although most research institutions are going more digital, clinical trials are usually documented manually. Paper-based routines have many significant downsides, which include erroneous data being stored in the general database and affecting the study. Implementing OCR might be game-changing in terms of improved time and cost efficiency, as well as flexibility. might improve several processes, from accurate data extraction and analysis to reporting. A solution with optical character recognition Other benefits may include: Data interpretability and operability Risk management Simplified monitoring Compliance with good practices Introducing handwritten text recognition to digitize paper-based documents used in clinical trials can save valuable resources, eliminate risks, improve security and improve the overall productivity of the conducted study. Another thing, using HTR can optimize data exchange between sponsor and investigator.