Previous research on the use of EMR data have focused on their convenience and accessibility. In EMR-based clinical research, the investigator has direct access to the EMR system and can quickly verify a hypothesis by sampling related variables from the system. EMR include clear diagnoses and prescriptions entered by physicians stored in a database that can be easily accessed and utilized.
EMR research can broadly reflect actual practice as it is fundamentally research aimed at the analysis of vast quantities of data already accumulated. More specifically, EMR enables researchers to extract and analyze quickly the side effects, 21 progress, and prognosis of drugs 22 with low frequency. EMR allows for the quick and systematic collection of data on the effectiveness or side effects that can manifest when a specific drug has been broadly prescribed to unspecified masses.
EMR research can practically approach a diverse range of safety issues not detected in three- and four-phase studies. With EMR data, any scholar can evaluate the actual achievement rate of goals of individual medical departments or hospitals. In other words, the one making the prescription is observed and data accumulated on what is being prescribed. Further, the diversification of approaches to data allows the evaluation of the prescription patterns of individuals.
Consequently, this system can be expected to enable the most appropriate prescriptions for any given disease. Thus, the possibility of approaching system-based rather than experience-based medicine is extremely high. A long period is necessary to conceptualize RCT for the effects or side effects of drugs that carry social issues. Indeed, there are cases in which clinical research is terminated owing to unexpected results of the RCT executed.
In such cases, a desirable RWE research model is one that presents the directionality of RCT by forecasting the research results under the concept of a pilot study by securing as extensive a range of data as possible in a short time period. Further, information on the prescribing patterns of physicians would be useful for establishing marketing plans. This point marks the most powerful advantage that can be done only with EMR.
Research can be conducted for all clinical trials only after having acquired the approval of an Institutional Review Board IRB. New topics of research outside the range of set guidelines cannot be approved by an IRB, and consequently, such research cannot be carried out. However, certain prescriptions that are outside the guidelines may exist within the actual EMR data. As RWE research uses already accumulated data, there is no infringement on the rights and benefits or harm with respect to the physical states of patients.
Therefore, research in itself is not impossible. Nonetheless, this aspect requires careful approach. Modification of existing guidelines can be proposed carefully, and additional large-scale RCT research can be proposed for this purpose. With deep learning becoming the talk of town in recent years, people are becoming increasingly interested in artificial intelligence that utilizes deep learning. As EMR have been implemented widely in hospitals, we expect a drastic increase in the analysis of accumulated EMR data.
To obtain clinically significant data from large hospitals, a clinical data warehouse can be an important tool, whereas EMR will potentially lead to various types of clinical research.
However, the majority of actual EMR-based deep learning projects have failed to reach the level initially anticipated. At the heart of the failure is data collection.
Various algorithms developed using unprocessed data have frequently failed to perform as anticipated. It is not possible to generalize and deduce outcomes with the results obtained from unprocessed and low-quality data. For these reasons, hospitals have recently commenced improving access to and systematic purification of data. Previously unknown information that clinical researchers fail to predict in the study planning phase may come to light, and EMR systems might lead to new knowledge and theories.
Accumulation of large-scale patient data and clinical trials using such data raise the important issue of the protection of personal information.
In the case of Korea, numerous limitations on the use of clinical big data have been enacted to address issues related to personal information leaks.
Therefore, privacy protection must be mentioned clearly when preparing works on RWD. This practice must include the fact that the name and resident registration number will not be included in the accumulated data, and information directly related to privacy, such as address, telephone number, and e-mail, will be removed. Any combination that can lead to the identification of an individual, such as hospital registration number and date of birth, must be removed immediately prior to the stage of analysis.
Further, all files on the extracted information must be encrypted and then stored in an encrypted computer, accessible only to the designated researcher. Additionally, contents of theses must not deal with the personal information of patients; researchers should emphasize that the research has no possibility of causing additional physical harm whatsoever to the subjects. Finally, it must be specified that the advance consent of the patient is not necessary given the anonymity of the subjects through encryption of data and that there is no effect on the rights and welfare of the subjects because past data, collected after having completed treatment, are used informed consent not required.
Research projects need to be commenced with a definitive design to minimize such biases. As extracted data could be deemed byproducts of the process of treating patients, rather than data elaborately organized and structured in accordance with the purposes of the research, the quality of the data can be low. There may even be many errors or erroneous inputting in the course of their collection. Through DQM, comparison with original data can be confirmed, including the mixed use of numbers and letters, erroneous descriptions, and serious errors.
This phase may be time consuming, depending on the number or capacity of data. The most serious bias normally occurs at this stage. As mentioned above, it is important to secure larger samples size to elevate fundamentally the reliability of RWE.
Although larger quantities of data do not necessarily elevate accuracy and reliability, using the same would help minimize bias that occurs in the smallest number. Even with plenty of actually extracted data, a researcher may end up excluding a large number once the data are sorted in accordance with the purpose of a research. Without baseline values, there can be no follow up, and changes in clinical laboratory data cannot be examined.
There also are frequent cases in which the actual overall number of research subjects is reduced by more than half, such as when patients for whom follow up is not possible or patients who transferred to another hospital in the middle of the research are excluded.
The exclusion of a large number of samples is in itself a significant bias, and it is the aspect of EMR-based clinical research pointed out most frequently by journal reviewers as a hard limitation. Certainly, not all the data are available in EMR. For one, it is difficult to confirm the compliance of the subjects using EMR, including whether they took drugs as instructed in a drug-related research.
It is often called a sugar pill. In clinical trials that include placebos, quite often neither patients nor their doctors know who is receiving the placebo and how is being treated with the experimental drug.
Many cancer clinical trials, as well as trials for other serious and life-threatening conditions, do not include placebo control groups. In these cases, all participants receive the experimental drug. Ask the trial coordinator whether there is a chance you may get a placebo rather than the experimental drug. Then, talk with your doctor about what is best for you.
Talk to the clinical trial coordinator to find out which phase the clinical trial is in. Learn more about the different clinical trial phases and whether they are right for you. Most drugs that undergo preclinical animal research never even make it to human testing and review by the FDA. The drug developers go back to begin the development process using what they learned during with their preclinical research.
Learn more about drug development. Learn more about the basics of clinical trial participation, read first hand experiences from actual clinical trial volunteers, and see explanations from researchers at the NIH Clinical Research Trials and You Web site.
What is Clinical Research. Address the needs of individual patients. Intended Benefit Generally designed and intended to benefit future patients. Intended to benefit the individual patient. Funding Paid for by drug developers and government agencies. Funded by individual patients and their health plans. Timeframe Depends on the research protocol. Requires real-time decisions.
Consent Requires written informed consent. May or may not require informed consent. Assessment Involves periodic and systematic assessment of patient data. Based on as-needed patient assessment. Protections Protected by government agencies, institutional review boards, professional standards, informed consent, and legal standards. Guided by state boards of medical practice, professional standards, peer review, informed consent, and legal standards. Certainty Tests products and procedures of unproven benefit to the patient.
Uses products and procedures accepted by the medical community as safe and effective. Access to Information Considered confidential intellectual property. Clinical researchers: NIH is launching a series of initiatives to enhance the accountability and transparency of clinical research. For information on the changes and how they will affect applicants and funded investigators, visit the Clinical Trial Requirements for Grants and Contracts section of the NIH website.
If you are conducting research with human subjects, we strongly recommend you review the updates and changes on the OER website, including the information on Basic Experimental Studies Involving Humans , to determine if they apply to your work. NIH maintains the ClinicalTrials. Each trial has its own pre-defined research plan or protocol, a specific goal or goals, and specific requirements for eligibility. The database includes studies that are looking for participants, in progress but not looking for participants, stopped, and completed.
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