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Researchers can use EHRs to retrieve up-to-date data from various sources around the country to advance their studies. EHRs can compute a report to show researchers certain trends in the population or common side effects of medications. <ref name="Enormous Benefits"></ref>

The EMR allows researchers to efficiently search patient medical information by medical condition, date of treatment, physician name and test category. Researchers can quickly focus their attention on medical information that will support their research efforts, develop databases to study patient outcomes, and cross-check complex medical information.

Researchers can use the EMR to analyze large amounts of patient data more efficiently, quickening the use of new research findings to improve patient care [5].

* EMR's increase the quality of medical data by recording coded rather than textual data. This, alongside the application of UMLS coding, will facilitate processes like data mining, data warehousing, ''in silico'' clinical trials, predictive modeling and any other mainstream research which requires data analysis. Also, by paving the way for automating data acquisition from other systems (like lab machines, imaging devices, barcode/RFID readers, bio-data sensors) error resulting from duplicate data entry procedures, manual file search and patient identification will decrease.

* While EMRs have shown an increase in the quality of medical data, research is still conflicting on the cost benefits and efficiency gains of EHRs. A study of HIMSS Analytics Database data from California medical-surgical units showed a decrease in cost efficiency for Stage 1 and Stage 2 EMR implementation, and no efficiency correlation for State 3 EMR implementations (http://www.ncbi.nlm.nih.gov/pubmed/20812460).

*EMRs contain large amounts of structured and free-text data which can be de-identified and used for research without disclosing patient information. Pantazos, K., Lauesen, S., Lippert, S. 2011. [http://www.ncbi.nlm.nih.gov.ezproxyhost.library.tmc.edu/pubmed/21893869 De-identifying an EHR Database - Anonymity, Correctness and Readability of the Medical Record]. Stud Health Technol Inform. 2011, 169, 862-866.

* In addition to structured vocabulary searches of EMR databases, free-text search algorithms within and EMR can generate additional information critical to the identification of epidemics. Often, critical information is omitted by the clinical team when only structured vocabulary is analyzed. <ref name="delisle 2010">DeLisle S, South B, Anthony JA, Kalp E, Gundlapalli A, Curriero FC, Glass GE, Samore M, Perl TM. Combining Free Text and Structured Electronic Medical Record Entries to Detect Acute Respiratory Infections. PLoS One''. 2010 Oct 14, 5(10):e13377. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2954790</ref>

=== Bioinformatics ===

* Genome-wide association studies have become commonplace for the identification of risk and causative genetic variants. The power of these studies is highly dependent on accurate phenotypic classification of both control and test populations. Application of natural language processing algorithms to free-text clinical narrative, in addition to structured data, can significantly benefit these studies. [http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995686/?tool=pmcentrez Kullo IJ, Fan J, Pathak J, Savova GK, Ali Z, Chute CG. Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease. ''J Am Med Inform Assoc.'' 2010 September, 17(5): 568-574.]

=== Translational Research Informatics (TRI) ===

Translational Research Informatics (TRI) is a sub-domain of biomedical informatics concerned with the application of informatics theory and methods to translational research (Translational research is the science is the project of bringing new knowledge from “bench to bedside.”) TRI mediates between and interoperates with the following: [http://en.wikipedia.org/wiki/Translational_research_informatics]

#Health Information Technology/ Electronic Medical Record systems

#Clinical Trial Management System /Clinical Research Informatics

#Statistical analysis and Data mining

=== Enhance public health surveillance ===

In addition to improving patient hospital outcomes, electronic health records can also improve public and population health outcomes as well. EHRs can accomplish this by improving reporting capabilities, ease the exchange of information across organizations, and improve communication between healthcare providers and public health officials. According to The Advisory Board Company, there are three key elements for successful population health management (The Advisory Board Company, 2014):

# Information-powered clinical decision making (e.g. robust patient data sets and integrated data networks)

# Primary care-led clinical workforce (e.g. PCP care team leaders and mobilization of community workforces)

# Patient engagement and community integration (e.g. map services to population need and overcoming non-clinical barriers to maximize health outcomes).

EHRs in conjunction with organizational improvement practices can help to address all three of these key elements. Incorporating electronic health records into public health practice not only improves public health surveillance, but also expands the communication between health care providers and public health professionals. In addition, organizations will be better able to track and prevent disease before an epidemic occurs. Through current government legislation, EHRs will assist public health research in achieving meaningful use(68). Many programs have already been implemented to begin this integration.

An example of successful EMR surveillance is displayed in a 2012 article of the American Journal of Preventive Medicine, where the study focused on a model EMR-based public health surveillance platform, Electronic Medical Record Support for Public Health (ESP). It was noted to enable clinicians to provide high-quality surveillance data on notifiable diseases, influenza-like illness, and diabetes to public health agencies. This surveillance data can help health departments acquire rich and timely data on broader populations and wider sets of health indicators than is routinely possible with current surveillance systems. [64]

In 2013, New York City Public Health Department is set to launch a project to aggregate EHR data into a surveillance tool to improve public health in the city [24]. This project will monitor the prevalence of conditions such as obesity, hypertension, smoking rates, and flu vaccinations.

=== Tracking Epidemics ===

Electronic Medical records have the potential to help patients get better care and hospitals leverage best practices on a large scale. But the ability to quickly and efficiently compile and analyze vast amounts of patient data is also of critical importance when it comes to spotting patterns in a health emergency or in fast spreading outbreaks, such as a flu pandemic or salmonella. The [[Centers for Disease Control and Prevention (CDC)|U.S. Centers for Disease Control and Prevention (CDC)]] and GE Healthcare are working on just that — with the official start of a project to evaluate putting EMR data to use in public health alerts. <ref name="emr cdc outbreak">http://www.gereports.com/using-emrs-to-help-the-cdc-track-outbreaks-faster/ </ref>

=== Improve Public Health Outcomes ===

EHRs can be very useful in managing health on groups of patients. Providers who have electronic health information about the entire population of patients, can look more meaningfully at the needs of patients who suffer from a specific condition and determine who are eligible for specific preventive measures and or currently taking specific medications This EHR capability helps providers identify and work with patients to manage specific risk factors or combinations of risk factors to improve patient outcomes.

<ref name="Improve Public Health Outcomes">http://www.healthit.gov/providers-professionals/improved-diagnostics-patient-outcomes</ref>

EHRs are beneficial to the Public Health and preventive sectors of healthcare. As they are able to perform syndomic surveillance data submission, immunization registries and electronic laboratory reporting.<ref name="Improve Public Health and population Outcomes">http://www.healthit.gov/providers-professionals/faqs/how-can-electronic-health-records-improve-public-and-population-health-</ref>Public health officials can monitor, manage and prevent disease easier and faster without headaches. Patients will be more compliant with immunizations as reminders will be used when a patient has missed an immunization or is in need of one. Patient follow up percentage rates will also increase which is very important in the public health and preventive sector of care as follow ups are hard to maintain through paper based record keeping. EHRs will aid an organization in need of maintaining compliance with state and national regulation of meaningful use standards.

=== Better Evidence Based Practices ===

The patient data stored electronically increases the availability of data, which may in turn lead to more quantitative analyses to identify evidence-based best practices more easily. With availability of the aggregated electronic clinic data, more public health researchers are using it for the research purposes to benefit the society. The availability of clinical data is limited, but as providers continue to implement EHRs, this pool of data will grow. By combining aggregated clinical data with other sources, such as over-the-counter medication purchases and school absenteeism rates, public health organizations and researchers will be able to better monitor disease outbreaks and improve surveillance of potential biological threats. [44]

EHR's use of clinical decision support systems could also decrease the time elapsed between acceptance of evidence-based research and actual practice of evidence-based medicine. A report from the Institute of Medicine, ''To Err is Human'', states that 15 years was the time frame that elapsed between acceptance of the evidence and practice. This time frame could be drastically reduced with electronic health resources. [52]

=== Pharmacogenetic Research ===

Patients' responses to drug treatment differ due to their genetic backgrounds. Such information is important to provide patient with optimized drug treatment.

EHR can improve the quality and efficiency of pharmacogenetic research works by providing the link between pharmacoepidemiology and pharmacogenetics. EHR also supporting the pharmacogenetic research with access to health record database. [http://www.ncbi.nlm.nih.gov/pubmed/24581153]

=== Clinical Research ===

'''How EMR’s Could Accelerate Clinical Trials (Front-end)''' [69]

#Study setup

##Query EMR database to establish number of potential study candidates.

##Incorporate study manual or special instructions into EMR “clinical content” for study encounters.

#Study enrollment

##An EMR can enable an organization to set up alerts so that a provider and/or study coordinator would receive an alert when a new patient is seen that qualifies for the study and prompt the provider to enroll that patient.<ref>EHR. http://www.sibley.org/epic/</ref>

#Implement study screening parameters into patient registration and scheduling.

##Query EHR database to contact/recruit potential candidates and notify the patient’s providers of potential study eligibility.

#Study execution

##Incorporate study specific data capture as part of routine clinical care/documentation workflows.

##Auto-populate study data elements into care report forms from other parts of the EMR database.

##Embed study specific data requirement as special tabs/documentation templates using structured data entry.

##Implement rules/alerts to ensure compliance with study data collection requirements.

##Create range checks and structured documentation checks to ensure valid data entry.

'''How EMR’s Could Accelerate Clinical Trials (Back-end)''' [69]

# Submission & Reporting

##Provide data extraction formats that support data exchange standards

##Document and report adverse events

#Evidence-based review

##Assess congruence of new findings and existing evidence with current practice and outcomes (incorporate into meta-analyses)

##Submit findings to electronic trial banks using published standards.

#Evidence-based clinical care

##Implement study findings as clinical documentation, order sets, point of care rules/alerts

##Monitor changes in care and outcomes in response to evidence base clinical decision support.

##Provide easy access to detailed clinical care data for motivating new clinical trial hypotheses.

=== The n-of-1 Clinical Trial ===

N-of-1 or single subject clinical trials consider an individual patient as the sole unit of observation in a study investigating the efficacy or side-effect profiles of different interventions. The ultimate goal of an n-of-1 trial is to determine the optimal or best intervention for an individual patient using objective data-driven criteria. The availability of electronically accessible data provides opportunities for learning from experience in clinical care; this can also referred to as evidence farming or using evidence macrosystem. Evidence farming can be characterized as a “bottom up” paradigm for clinical practices to incorporate practice data systematically as source of evidence, or and articulated form of clinical experience. <ref name="n-of-1">Lillie, Elizabeth O., et al. "The n-of-1 clinical trial: the ultimate strategy for individualizing medicine?" http://www.ncbi.nlm.nih.gov/pubmed/21695041 </ref>

=== Clinical Data Research Networks ===

Since electronic medical records systems allow for the capture and storage of records in a discrete data format many secondary uses of the data is made possible. By utilizing health information exchange communities can share and aggregate their data for research to improve population health. The compiled data can be used to improve patient engagement, improve regulatory oversight, share the results of studies across health systems, and increase the use of research to improve outcomes at member institutions. In New York City this very concept has been proven successful through a project funded by the Patient-Centered Outcomes Research Institute (PCORI), and with the future adoption and utilization of HIEs more populations will be able to take advantage of these benefits. <ref name="CDRN">Kaushal, R., Hripcsak, G., Ascheim, DD., et al. (2014, March 25). Changing the research landscape: the New York City Clinical Data Research Network. J Am Med Inform Assoc. doi:10.1136/amiajnl-2014-002764</ref>

=== Improved Reporting Capabilities ===

An EMR has the capability of providing a more robust reporting environment with integrated clinical and administrative data, standardized clinical assessments and calculation of outcome measures[http://ptjournal.apta.org/content/86/3/434.full.pdf+html].

Facing more and more complicated situation in clinical areas, doctors and other people need more up-to date data and knowledge to help them make decision. Thus, they use clinical decision support system (CDSS) to help them getting up-to-date information and selecting more appropriate remedy. EMR and facilitate this process by providing just-in-time data. In the end, practitioners can apply evidence-based medicine by EMR and CDSS.

For example, surveys performed in resources-constrained areas like Kenya about HIV show that EMR based CDSS by many ways like Increasing Guideline adherence, reducing data errors, decreasing patient visit time, and ects. Researchers from King Saud University in Saudi Arabia also found the usefulness of incorporating EHR techniques in their clinical decision support systems. The team created a four-module knowledge-based system that incorporated algorithmic guidelines and EHR data mining (66). Guidelines used in the proposed system are the International Classification of Disease (IDC), SNOMED CT, LOINIC, and the Unified Medical Language System (UMLS). The sophisticated system is projected to not only increase workflow, but also serve as a system for various entities to use as a consulting tool.

===Structure to Clinical Environment===

Clinical care outcomes may be improved by promoting the use of electronic checklists in clinical settings. A study from John Hopkins demonstrates a 0% bloodstream infection rate from intravenous lines after checklists were adopted as procedure. In addition, this lowered infection rate and also reduced medical costs that may have otherwise been associated with bloodstream infections. Another study showed reduced errors in positioning by surgeons for laparoscopic procedures.

Major goals of checklists:

*To educate

*To serve as action reminders

*To promote teamwork for best practices

*To capture clinical data for reporting purposes

Electronic checklists are able to accommodate for any supplementary photos, images and documents with consistent formatting and can be found in a single and readily accessible location. <ref name="EHRI 2013">Improving Patient Care with Structured Clinical Care. http://www.youtube.com/watch?v=PMv7kKoGir8#t=419</ref>

== References ==



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