Presentation 7
a. Knowledge Generation Through Nursing Research
Information literacy is an intellectual framework for finding, understanding,
evaluating, and using information. These activities are accomplished in part through
fluency with information technology and sound investigative methods, but most
importantly through critical reasoning and discernment. The ACRL (2016) has
suggested that “information literacy initiates, sustains, and extends lifelong learning
through abilities which may use technologies but are ultimately independent of them”
As nursing informatics (NI) combines all four nursing practice areas (clinical,
research, administration, and education), the ability to recognize the need for a
specific kind of information and then locate, evaluate, and effectively use that
information within the NI paradigm will catapult nurses ahead of other healthcare
professionals in applying and engaging various facets of technology (ACRL, 2016).
However, because so few nurses have formal training in technology but still represent
a disproportionate number of users, the ways in which nursing research integrates
healthcare technology within NI creates an unseen challenge (McHaney, 2007). This
potentially enormous impact on the future of health care and technology will
determine the success of informationliterate nurses: those who have learned how to
learn and who understand the intricacies of how knowledge is organized, retrieved,
and used in such a way that others can learn from them (ACRL, 2016).
It is increasingly important that nurse educators take proactive steps to prepare
nursing students for information-literate practice in the technology-laden healthcare
environments of today. As the healthcare industry continues to evolve with rapid
advancements in technology, the ability to effectively use and manage information has
become a critical skill for nursing professionals. In their influential work, Stephens-
Lee and colleagues (2013) emphasized the necessity of integrating Nursing
Informatics (NI) into nursing education, stating that “[n]ursing students require
opportunities to help them develop NI skills and abilities to prepare them for
contemporary workplaces.” This call to action highlights the essential role of nurse
educators in equipping students with the competencies needed to thrive in modern
clinical settings.
The concept of information literacy in nursing encompasses a wide range of
skills, including the ability to locate, evaluate, and use information effectively. In a
healthcare context, this means being proficient in accessing and interpreting electronic
health records (EHRs), utilizing clinical decision support systems, managing patient
data securely, and staying abreast of the latest medical research and evidence-based
practices. Nurse educators must therefore design curricula that not only cover clinical
skills and theoretical knowledge but also emphasize the integration of informatics and
technology into everyday nursing practice.
To achieve this, educators can implement several strategies that provide
students with hands-on experience and practical knowledge. One effective approach is
the incorporation of simulation-based learning, which allows students to engage with
realistic clinical scenarios in a controlled environment. Through simulations, students
can practice using EHRs to document patient care, retrieve vital patient information,
and make informed decisions based on digital data. These exercises help students
become comfortable with the technology they will encounter in real healthcare
settings and develop the ability to use it efficiently and accurately.
Furthermore, nurse educators can enhance information literacy by integrating
assignments and projects that require the use of informatics tools. For example,
students might be tasked with conducting literature reviews using online medical
databases, developing patient care plans using clinical decision support tools, or
analyzing patient data to identify trends and inform care decisions. These activities
encourage students to engage with digital resources actively and develop the critical
thinking skills necessary to interpret and apply information in their practice.
In addition to practical applications, theoretical instruction on informatics
principles is also crucial. Courses on healthcare informatics should cover topics such
as data management, cybersecurity, health information systems, and the ethical
considerations related to digital health information. Understanding these concepts
provides a strong foundation for students, enabling them to navigate the complexities
of technology in healthcare with confidence and competence.
Collaborative learning is another valuable method for fostering information
literacy. By working in interdisciplinary teams on projects and simulations, nursing
students can learn how to communicate and collaborate effectively with other
healthcare professionals, such as physicians, pharmacists, and IT specialists. This
teamwork not only mirrors the collaborative nature of real-world healthcare
environments but also highlights the importance of integrating diverse expertise to
provide comprehensive patient care.
Moreover, continuous professional development opportunities for nurse
educators themselves are essential to ensure that they are well-versed in the latest
technological advancements and educational methodologies. Professional
development programs, workshops, and conferences focused on nursing informatics
and educational technology can help educators stay current and bring fresh, relevant
knowledge back to their students.
The importance of preparing nursing students for information-literate practice
extends beyond individual competency; it has significant implications for patient
safety and quality of care. Inaccurate documentation, misinterpretation of data, and
lack of timely access to patient information can lead to errors and adverse outcomes.
By ensuring that nursing graduates are adept at using informatics tools and managing
information effectively, educators play a crucial role in mitigating these risks and
promoting better patient outcomes.
Additionally, as healthcare continues to shift towards value-based care models
that emphasize efficiency, patient outcomes, and cost-effectiveness, the ability to use
information technology to optimize care processes becomes increasingly valuable.
Nurses who are proficient in informatics can contribute to more efficient care
delivery, reduce redundancies, and enhance patient satisfaction, aligning with broader
healthcare goals and reforms.
Lastly, fostering a culture of information literacy and continuous learning
within nursing education encourages students to become lifelong learners. The field of
healthcare is ever-changing, with new technologies, treatments, and best practices
emerging regularly. Nurses who are comfortable with technology and information
management are better equipped to adapt to these changes and continuously improve
their practice, ultimately contributing to the advancement of the nursing profession.
In summary, it is imperative that nurse educators prepare nursing students for
information-literate practice in technology-laden healthcare environments. By
providing opportunities to develop Nursing Informatics skills, incorporating
simulation-based learning, emphasizing theoretical instruction, promoting
collaborative learning, and engaging in continuous professional development,
educators can ensure that their students are well-prepared for contemporary
workplaces. This preparation is crucial for enhancing patient care, ensuring safety,
and meeting the demands of modern healthcare systems. As Stephens-Lee and
colleagues (2013) aptly noted, equipping nursing students with these skills is essential
for their success and the advancement of the healthcare field.
Nursing faculty must be able to adapt their teaching in this new technology era
so that they engage their students and help them assimilate technology as they prepare
for their professional nursing roles. Focusing on nurses providing direct patient care,
Piscotty et al. (2015) stated that “finding methods that can help nurses offer safe and
effective care using technology is an absolute necessity” (p. 287). As nurses enact
their role as administrators, researchers, educators, and/or clinical staff members,
integrating technology in the current technology-laden information era is paramount.
b. Acquiring Previously Gained Knowledge Through Internet and Library
Holdings
In an environment characterized by rapid technological change coupled with
an overwhelming proliferation of information sources, nurses face an enormous
number of options when choosing how and from where to acquire information for
their academic studies, clinical situations, and research. Because information is
available through so many venues, libraries, specialinterest organizations, media,
community resources, and the Internet in increasingly unfiltered formats, healthcare
practitioners must inevitably question the authenticity, validity, and reliability of
information.
Often, the retrieval of reliable research and information may seem to be a
daunting task in light of the seemingly ubiquitous amount of information found on the
Web. Focusing on specific information venues not only aids this search but also
assists in negotiating the endless maze of resources, allowing a nursing practitioner to
find the best and most accurate information efficiently.
Professional databases represent a source of online information that is
generally invisible to all Internet users except those with professional or academic
affiliations, such as faculty, staff, and students. These databases, which range from
specific to general, act as collection points by aggregating information, such as
abstracts and articles from many different journals; two such databases include the
Cumulative Index to Nursing and Allied Health Literature (CINAHL) and MEDLINE.
CINAHL, for example, specifically includes information from all aspects of allied
health, nursing, alternative medicine, and community medicine. The MEDLINE
database contains references to more than 22 million journal articles and is maintained
and produced by the National Library of Medicine. Other databases, such as PsycInfo
from the American Psychological Association and the Educational Resources
Information Center (ERIC) database, may also benefit nursing. Many databases also
offer full-text capabilities, meaning that entire articles are available online. The
articles and abstracts contained within these databases have already withstood the
rigors of publication in professional journals and, therefore, are considered viable and
authentic peer-reviewed sources.
Libraries with subscriptions to databases often employ library professionals
who are able to help patrons sift through the vast amounts of available electronic
information; using the expert research capabilities of a health science librarian at
one’s local university is the best way to learn how to conduct database searches that
yield the most efficient and useful results. Also useful are websites that provide
tutorials on best searching practices specifically for medically oriented databases,
such as the tutorials provided by EBSCO support to search the CINAHL database.
Search engines allow users to surf the Web and find information on nearly
anything, although many researchers steer clear of search engines because of the vast
amounts of unsubstantiated information they are likely to uncover. Because no
legitimacy needs to be provided for any information that appears on the Web, an
author can make claims, substantiated or not, and still use the Web as a publishing
venue. Despite the pitfalls associated with search engines in general, they can yield a
bounty of useful information when used with discretion. Different search engines will
produce different results when used for the same research. For example, one popular
search engine ranks its results by number of hits a page or site has received. Whereas
the most popular research results are likely to be relevant, the order in which results
appear does not indicate quality or viability of the source.
Different Web address (domain) suffixes (.com, .edu, .org, .gov, and so forth)
indicate who is responsible for creating the website. Although a .edu site is hosted by
an educational institution and for that reason may seem legitimate, consider that it
could also belong to a student stating personal opinion, gossip, or guesswork. In
contrast, .gov sites are maintained by the government and nearly always have
professional contact information. Web hosts develop new domain suffixes constantly,
so although looking at the suffix can be useful, it should not be the sole deciding
factor when choosing to trust information.
One should never blindly trust information found on a webpage. When
possible, check the date of the most recent update (How old is the page?), contact
information (Is a bibliography or list of sources provided?), links to external sources
(Do they seem relevant?), and previous attained knowledge from other reputable
sources (Is the information too unbelievable?). Fees and information retrieval charges
should be approached with skepticism. Private companies do offer information
aggregation services for a fee. In these cases, users pay a flat monthly fee for access to
collections of articles in a particular field. What users (especially those affiliated with
an academic institution) may not realize is that they are likely to have free access to
the same, if not more complete, information through their institution’s library system.
Some legitimate databases and traditional newspapers that maintain a Web
presence do provide access for a small fee, but just as many others simply ask users to
register to see articles for free. Many nursing students and professionals affiliated
with a university may find that their university library has already purchased access
for the student body.
Nearly all higher education institutions have placed their library catalogs
online. Although this is an obvious convenience for many students, some nursing
professionals unused to working completely online may be intimidated by an e-
catalog. Library professionals at the tiniest university and the busiest community
college are available to demonstrate how to navigate a basic search of their library’s
catalog. Asking for assistance in learning how to access the vast assortment of
journals, books, databases, and other resources available at one’s college library is an
excellent idea. Students in nursing programs at larger universities will likely find free
classes that specifically teach users how to navigate and use the online catalog. If
smaller colleges and universities do not offer these services, one should take
advantage of the library’s online tutorials, help pages, frequently asked questions
pages, and online reference service (if available). Local public libraries often have
subscriptions to popular databases and offer free classes on searching techniques to
patrons, providing yet another free access point to the best information for one’s
research needs. Making full use of available library resources serves to strengthen
information literacy skills, enabling learners to master content and extend their
investigations, become more self-directed, and assume greater control over their own
learning.
c. KDD and Research
According to IBM (2013), big data does not just refer to size, but rather “is an
opportunity to find insights in new and emerging types of data and content, to make
your business more agile, and to answer questions that were previously considered
beyond your reach” (p. 6). Data mining for knowledge discovery is not new; it has
been used for some time in research. However, we now have advanced analytical
software designed to facilitate data mining. The knowledge discovery capabilities
continue to evolve. Goodwin et al. (1997), for example, reported on their
collaborative data mining research, which explored the relationship between clinical
data and adult respiratory distress syndrome (ARDS) in critically ill patients. DeGruy
(2000) indicated that big data in health care needed to be analyzed using KDD tools
and applications to determine trends and relationships, with the ultimate goal of
decreasing healthcare costs while improving quality.
Berger and Berger (2004) suggested that nurse researchers are uniquely
positioned to leverage data mining technologies to transform the vast repositories of
big data into comprehensible and actionable knowledge. This transformation is crucial
for guiding nursing practice and facilitating interdisciplinary research, as it enables
healthcare professionals to extract meaningful insights from the overwhelming
volumes of data generated in clinical settings. The advent of big data in healthcare has
opened up unprecedented opportunities for improving patient outcomes, optimizing
care delivery, and advancing medical research. However, the sheer volume and
complexity of this data necessitate sophisticated analytical tools and methodologies to
make sense of it all.
Data mining, a key component of big data analytics, involves the use of
algorithms and statistical techniques to identify patterns, correlations, and trends
within large datasets. For nurse researchers, the application of data mining
technologies offers several significant benefits. Firstly, it allows for the identification
of subtle patterns and trends that may not be immediately apparent through traditional
data analysis methods. For example, data mining can reveal associations between
patient characteristics and health outcomes, uncovering factors that contribute to
disease progression or recovery. This information can be instrumental in developing
targeted interventions and personalized treatment plans that improve patient care.
Moreover, data mining enables the analysis of unstructured data, such as
clinical notes, patient narratives, and other text-based information, which comprise a
substantial portion of healthcare data. Natural language processing (NLP) techniques
can be employed to extract valuable insights from these unstructured sources,
providing a more comprehensive understanding of patient conditions and experiences.
By integrating structured and unstructured data, nurse researchers can gain a holistic
view of patient health and uncover new avenues for research and practice
improvement.
The ability to analyze large-scale data also supports evidence-based practice, a
cornerstone of modern nursing. By systematically reviewing and synthesizing data
from diverse sources, nurse researchers can identify best practices and generate robust
evidence to inform clinical guidelines and protocols. This evidence-based approach
ensures that nursing interventions are grounded in the latest scientific knowledge,
enhancing the quality and effectiveness of care.
Furthermore, data mining facilitates interdisciplinary research by enabling
collaboration between nursing and other healthcare disciplines. The complex nature of
healthcare challenges often requires the integration of insights from multiple fields,
including medicine, public health, pharmacology, and social sciences. Data mining
technologies provide a common platform for analyzing and interpreting data across
these disciplines, fostering a collaborative approach to problem-solving. For instance,
nurse researchers can work alongside epidemiologists to study disease patterns,
collaborate with pharmacologists to evaluate medication efficacy, or partner with
public health experts to address social determinants of health.
In addition to improving clinical practice and research, data mining can also
contribute to healthcare management and policy development. By analyzing
operational data from healthcare organizations, nurse researchers can identify
inefficiencies, predict patient demand, and optimize resource allocation. These
insights can inform strategic decisions and policy initiatives aimed at enhancing
healthcare delivery and reducing costs. For example, predictive analytics can be used
to forecast hospital admissions, enabling better staffing and resource planning.
The potential of data mining in nursing research extends to the realm of public
health as well. Large-scale data analysis can uncover population health trends,
monitor disease outbreaks, and evaluate the impact of public health interventions.
Nurse researchers can use these insights to advocate for evidence-based public health
policies and programs that address community health needs and disparities.
Despite the promise of data mining technologies, there are several challenges
that nurse researchers must navigate to fully realize their potential. Data quality and
integrity are paramount, as the accuracy of data mining results depends on the
reliability of the underlying data. Ensuring data privacy and security is also critical,
given the sensitive nature of healthcare information. Nurse researchers must adhere to
ethical standards and regulatory requirements to protect patient confidentiality and
data security.
Additionally, the complexity of data mining techniques necessitates
specialized skills and knowledge. Nurse researchers need to be proficient in data
analytics, statistics, and information technology to effectively utilize data mining
tools. Interdisciplinary education and training programs can equip nurse researchers
with the necessary competencies, enabling them to harness the power of big data for
nursing practice and research.
In summary, Berger and Berger (2004) highlighted the transformative
potential of data mining technologies for nurse researchers. By converting vast
repositories of big data into comprehensible knowledge, nurse researchers can
enhance nursing practice, support evidence-based care, and facilitate interdisciplinary
research. The integration of data mining in nursing research holds the promise of
improving patient outcomes, optimizing healthcare delivery, and advancing the
frontiers of medical science. As the healthcare landscape continues to evolve, the
strategic use of data mining technologies will be essential for addressing complex
healthcare challenges and driving innovation in nursing and beyond.
Over the last several decades, the KDD capability has improved as the
analytical power of data mining tools has increased, thereby facilitating the
recognition of patterns and relationships in big data. Trangenstein, Weiner, Gordon,
and McNew (2007) described how their faculty used data mining to analyze their
students’ clinical logs, which enabled them to make programmatic decisions and
revisions and rethink certain clinical placements. Zupan and Demsar (2008) described
the open source tools developed by data mining researchers that they felt were ready
to be used in biomedical research. Fernández-Llatas et al. (2011) described workflow
mining technology as a means to facilitate relearning in dementia processes.
Based on the results of this data mining analysis, nurses can better target their
interventions to prevent pressure ulcers. Green et al. (2013) identified differences in
limb volume patterns in breast cancer survivors; their results have the potential to
influence clinical guidelines for the assessment of latent and early-onset lymphedema.
In a large teaching hospital, there is a high rate of readmission for patients
with congestive heart failure (CHF) who are being treated at the facility. The chief
nursing officer (CNO) wants to know the cause of the readmissions, as the rate at this
facility is almost twice that of competing healthcare entities in the area. The CNO
works with the nursing researchers at the university to address this situation.
The researchers begin to scour the electronic health records (EHRs) of more
than 15,000 CHF hospitalizations in the past 4 years to determine the cause of the
situation. As they begin to understand this dataset, they are able to build a data mining
model using algorithms to discover patterns and relationships in the data. Based on
the old data, they determine that the key factor for readmission was the length of time
it took to follow up at home with discharged patients. In response to this new
knowledge, a program in which nurses contact patients with CHF the day after their
discharge by phone was developed, and a home visit is scheduled for the second day
postdischarge to ensure a smooth transition to home or an assisted living facility. This
followup within the first 4 days of discharge has reduced readmissions by 40%. The
model that was used with the old data is being applied to the new data.
d. Data Mining Concepts
Bagging is a term for the use of voting and averaging in predictive data mining
to synthesize the predictions from many models or methods or the use of the same
type of a model on different data. This term can also refer to the unpredictability of
results when complex models are used to mine small datasets.
Boosting is what the term infers—a means of increasing the power of the
models generated by weighting the combinations of predictions from those models
into a predicted classification. This iterative process uses voting or averaging to
combine the different classifiers. Data reduction shrinks large datasets into
manageable, smaller datasets. One way to accomplish this is via aggregation of the
data or clustering.
Drill down analysis typically begins by identifying variables of interest to drill
down into the data. You could identify a diagnosis and drill down, for example, to
determine the ages of those diagnosed or the number of males. You could then
continue to drill down and expose even more of the data.
Exploratory data analysis (EDA) is an approach or philosophy that uses
mainly graphical techniques to gain insight into a dataset. Its goal varies based on the
purpose of the analysis, but EDA can be applied to the dataset to extract variables,
detect outliers, or identify patterns. Feature selection reduces inputs to a manageable
size for processing and analysis, as the model either chooses or rejects an attribute
based on its usefulness for analysis.
Machine learning s a subset of artificial intelligence that permits computers to
learn either inductively or deductively. Inductive machine learning is the process of
reasoning and making generalizations or extracting patterns and rules from huge
datasets—that is, reasoning from a large number of examples to a general rule.
Deductive machine learning moves from premises that are assumed true to
conclusions that must be true if the premises are true.
Meta-learning combines the predictions from several models. It is helpful
when several different models are used in the same project. The predictions from the
different classifiers or models can be included as input into the meta-learning. The
goal is to synthesize these predicted classifications to generate a final, bestpredicted
classification—a process also referred to as stacking.
Predictive data mining is a sophisticated analytical technique focused on the
identification and development of models that can forecast future outcomes based on
historical data. This approach is particularly valuable in a wide range of fields,
including healthcare, finance, marketing, and more, where the ability to anticipate
trends and behaviors can lead to significant advantages. In the context of a data
mining project, the primary goal is to construct a predictive model that can accurately
classify new instances based on patterns learned from existing data. This involves a
series of steps including data collection, preprocessing, model selection, training,
evaluation, and ultimately, deployment.
The process begins with the collection of a comprehensive dataset, which
serves as the foundation for model development. This dataset typically includes a
variety of features or variables that are believed to influence the outcome of interest.
For instance, in healthcare, these features might include patient demographics,
medical histories, lab results, and treatment plans. Once the data is collected, it
undergoes preprocessing to clean and prepare it for analysis. This step is crucial as it
involves handling missing values, removing duplicates, normalizing data, and
transforming variables into formats suitable for modeling.
After preprocessing, the next step is selecting the appropriate predictive
modeling techniques. There are numerous algorithms available for predictive data
mining, each with its strengths and weaknesses. Common techniques include decision
trees, random forests, support vector machines, neural networks, and logistic
regression, among others. The choice of algorithm depends on various factors,
including the nature of the data, the complexity of the problem, and the desired
balance between interpretability and predictive power.
Once a suitable algorithm is chosen, the model is trained using a subset of the
data. During training, the model learns to recognize patterns and relationships within
the data that can be used to make predictions about new, unseen instances. This
process involves optimizing the model's parameters to minimize prediction error and
improve accuracy. Following training, the model is evaluated using a separate
validation dataset to assess its performance and ensure it generalizes well to new data.
One advanced technique within predictive data mining is stacking, also known
as stacked generalization. Stacking involves combining the predictions of multiple
individual models to create a more robust and accurate composite model. The
rationale behind stacking is that different models may capture different aspects of the
data, and by synthesizing their predictions, the overall performance can be improved.
This technique can be particularly powerful in complex prediction tasks where no
single model consistently outperforms others across all scenarios.
The stacking process typically involves two levels of modeling. In the first
level, several base models are trained independently on the dataset. These base
models can be of various types, such as decision trees, neural networks, and support
vector machines. Each base model generates its predictions, which are then used as
input features for the second level, known as the meta-model. The meta-model is
trained to learn how to best combine the predictions of the base models, effectively
synthesizing their strengths while mitigating their weaknesses.
The effectiveness of stacking depends on the diversity and complementary
nature of the base models. By leveraging different algorithms and perspectives,
stacking can capture a wider array of patterns and nuances in the data. This diversity
helps to reduce overfitting and improves the model's ability to generalize to new,
unseen data.
Predictive data mining and techniques like stacking have significant
implications in various domains. In healthcare, for instance, predictive models can be
used to anticipate patient outcomes, identify high-risk patients, and tailor personalized
treatment plans. This can lead to improved patient care, better resource allocation, and
reduced healthcare costs. In finance, predictive models can forecast stock prices,
assess credit risk, and detect fraudulent transactions, thereby enhancing decision-
making and risk management.
Furthermore, predictive data mining can be instrumental in marketing by
enabling businesses to predict customer behavior, segment their audience, and
optimize marketing campaigns. By understanding which customers are likely to
churn, make purchases, or respond to promotions, companies can develop targeted
strategies that increase customer engagement and loyalty.
In summary, predictive data mining is a powerful approach aimed at creating
models that can forecast future outcomes based on historical data. This involves a
meticulous process of data collection, preprocessing, model selection, training, and
evaluation. Stacking, or stacked generalization, further enhances predictive accuracy
by combining the strengths of multiple models. These techniques are invaluable
across various sectors, including healthcare, finance, and marketing, where they drive
better decision-making, optimize operations, and deliver significant benefits. As
technology and data availability continue to advance, the potential applications and
impact of predictive data mining will only grow, offering new opportunities for
innovation and improvement.
e. Clarification of Terms
Evidence-based practice, translational research, and research utilization are all
terms that have been used to describe the application of evidential knowledge to
clinical practice. The following paragraphs explore the definitions of each term.
Although these terms are related, they have slightly different meanings and
applications. Evidence-based practice (EBP), developed originally for its application
to medicine, is defined by Sackett et al. (1996) as “The conscientious, explicit and
judicious use of current best evidence in making decisions about the care of individual
patients” (p. 71). The “best evidence” in this context refers to more than just research.
Goode and Piedalue (1999) stated that evidence-based practice should be combined
with other knowledge sources and “involves the synthesis of knowledge from
research, retrospective or concurrent chart review, quality improvement and risk data,
international, national, and local standards, infection control data, pathophysiology,
cost effectiveness analysis, benchmarking data, patient preferences, and clinical
expertise” (p. 15). EBP starts with a clinical question to resolve a clinical problem.
For example, published research studies are used in healthcare quality
initiatives as the evidence behind the development of practice algorithms designed to
decrease practice variability, increase patient safety, improve patient outcomes, and
eliminate unnecessary costs. Use of EBP promotes the use of clinical judgment and
knowledge in relation to the patient’s contextual situation and preferences, with
procedures and protocols being linked to scientific evidence rather than based on what
is customary practice or opinion.
Difficulties in translating research to the T2 setting exist when research
applications do not fit well within the clinical context or practical considerations
constrain the application in a clinical setting. Translational research is complicated by
the follow-up analysis, practice, and policy changes that occur when adopting
research into practice; consequently, available evidence-based healthcare practices are
often not fully incorporated into daily care (Titler, 2004, 2010). Organizational culture
influences the changes made to a clinical application and establishes the groundwork
and the support for change-making activities (Titler, 2004). The study of ways to
promote the adoption of evidence in the healthcare context is called “translation
science”
Translational bioinformatics is the “development of storage, analytic, and
interpretive methods to optimize the transformation of increasingly voluminous
biomedical data, and genomic data, into proactive, predictive, preventive, and
participatory health (American Medical Informatics Association [AMIA], 2016c,
para. 1). It integrates biological and clinical data and the evolution of clinical
informatics methodology to include biological observations. “The end product of
translational bioinformatics is newly found knowledge from these integrative efforts
that can be disseminated to a variety of stakeholders, including biomedical scientists,
clinicians, and patients” (AMIA, 2016c, para. 1).
Clinical informatics is an interdisciplinary field that involves the application of
informatics and information technology to enhance the delivery of healthcare
services. It is a critical component of modern healthcare, integrating data,
information, and knowledge to support and improve clinical decision-making, patient
care, and health outcomes. Clinical informatics, also known as applied clinical
informatics and operational informatics, encompasses a broad range of activities and
tools designed to manage and utilize health information effectively and efficiently.
The primary goal of clinical informatics is to leverage technology to support
the work of healthcare providers, improve patient safety, and enhance the quality of
care. This involves the development and implementation of electronic health records
(EHRs), clinical decision support systems (CDSS), computerized provider order entry
(CPOE) systems, and other health information technologies (HIT). By facilitating the
seamless exchange and analysis of health data, clinical informatics helps healthcare
professionals make more informed decisions, streamline workflows, and reduce the
risk of errors.
One of the key aspects of clinical informatics is the implementation and
optimization of electronic health records. EHRs are digital versions of patients' paper
charts and contain comprehensive information about a patient's medical history,
diagnoses, medications, treatment plans, immunization dates, allergies, radiology
images, and laboratory test results. By providing a centralized and easily accessible
repository of patient information, EHRs enable healthcare providers to quickly
retrieve and review pertinent data, enhancing the coordination and continuity of care.
Moreover, EHRs facilitate communication and information sharing among different
healthcare providers and organizations, ensuring that all members of the care team
have access to the same up-to-date information.
Clinical decision support systems are another vital component of clinical
informatics. CDSS are designed to assist healthcare providers in making clinical
decisions by providing evidence-based knowledge and patient-specific information at
the point of care. These systems can offer recommendations for diagnostic tests, alert
providers to potential drug interactions, suggest appropriate treatments, and remind
clinicians of preventive care guidelines. By integrating clinical guidelines and
protocols into the workflow, CDSS help ensure that care is consistent with the best
available evidence, ultimately improving patient outcomes.
Computerized provider order entry systems are also integral to clinical
informatics. CPOE systems enable healthcare providers to enter medical orders, such
as prescriptions, laboratory tests, and imaging studies, directly into a computer
system. This eliminates the need for handwritten orders, reducing the risk of
transcription errors and improving the accuracy and legibility of orders. CPOE
systems often include decision support features that can check for potential errors,
such as drug allergies, drug interactions, and dosage errors, further enhancing patient
safety.
In addition to these core technologies, clinical informatics encompasses a wide
range of other tools and applications designed to support various aspects of healthcare
delivery. For example, telemedicine platforms enable remote consultations and
monitoring, allowing patients to receive care from the comfort of their homes. Mobile
health applications can help patients manage chronic conditions, track their health
metrics, and communicate with their healthcare providers. Data analytics and machine
learning algorithms can analyze large datasets to identify trends, predict outcomes,
and inform population health management.
Clinical informatics also plays a crucial role in quality improvement and
healthcare research. By systematically collecting and analyzing health data,
informatics professionals can identify areas for improvement, track the impact of
interventions, and generate new insights into disease processes and treatment efficacy.
This evidence-based approach supports continuous learning and improvement within
healthcare organizations, ultimately leading to better patient outcomes and more
efficient care delivery.
The field of clinical informatics is continuously evolving, driven by advances
in technology and the growing recognition of the importance of data in healthcare. As
new technologies emerge and healthcare systems become increasingly complex, the
demand for skilled clinical informaticians continues to grow. These professionals,
who often have backgrounds in medicine, nursing, computer science, or information
management, play a critical role in designing, implementing, and optimizing health
information systems.
Moreover, the integration of clinical informatics into healthcare education and
training is essential to prepare the next generation of healthcare professionals for the
digital age. By incorporating informatics principles into medical and nursing
curricula, educational institutions can equip future providers with the knowledge and
skills needed to effectively use technology in their practice.
In summary, clinical informatics is the application of informatics and
information technology to deliver healthcare services, encompassing a wide range of
activities aimed at improving clinical decision-making, patient care, and health
outcomes. It involves the implementation and optimization of electronic health
records, clinical decision support systems, computerized provider order entry systems,
and other health information technologies. By leveraging these tools, clinical
informatics enhances the coordination and continuity of care, supports evidence-based
practice, improves patient safety, and facilitates quality improvement and research. As
the healthcare landscape continues to evolve, the role of clinical informatics will
become increasingly important in ensuring that healthcare systems can meet the
challenges of the future.
Clinical research informatics is defined by AMIA as involving the use of
informatics in the discovery and management of new knowledge relating to health and
disease. “It includes management of information related to clinical trials and also
involves informatics related to secondary research use of clinical data. Clinical
research informatics and translational bioinformatics are the primary domains related
to informatics activities to support translational research”
f. History of Evidence-Based Practice
Research results are crucial to furthering EBP. The concept of using
randomized controlled trials and systematic reviews as the gold standard against
which one should evaluate the validity and effectiveness of a clinical intervention was
introduced in 1972 by Archie Cochrane, a scientist and a physician (Cochrane, 1972).
Cochrane’s experiences as a prisoner of war and medical officer while interning
during World War II led to his belief that not all medical interventions were needed
and some caused more harm than good.
Cochrane's perspective on the randomized clinical trial (RCT) as a cornerstone
of validating clinical interventions and promoting evidence-based medicine has had a
profound impact on modern healthcare practices. His advocacy for rigorous scientific
evaluation aimed to ensure that medical treatments and interventions were not only
effective but also based on solid empirical evidence, thereby enhancing patient care
and safety.
Randomized clinical trials represent a gold standard in clinical research
methodology. They are designed to systematically evaluate the efficacy and safety of
medical interventions by randomly assigning participants to either the treatment group
or the control group (placebo or standard treatment). This randomization helps
minimize bias and ensures that any observed differences in outcomes between groups
can be attributed to the intervention being studied rather than other factors.
Cochrane recognized the importance of RCTs in providing reliable evidence to
guide clinical decision-making. By comparing outcomes between intervention and
control groups under controlled conditions, RCTs help determine whether a treatment
or intervention produces beneficial effects, adverse effects, or no significant effects at
all. This rigorous evaluation process is crucial for identifying which interventions are
scientifically supported and should be adopted into clinical practice.
Moreover, Cochrane emphasized the necessity of limiting clinical
interventions to those that are not only effective but also necessary. This perspective
underscores the importance of avoiding unnecessary treatments, which can expose
patients to potential risks, increase healthcare costs, and divert resources from more
beneficial interventions. RCTs play a pivotal role in distinguishing between
interventions that provide genuine clinical benefit and those that do not, thereby
promoting the rational and evidence-based use of healthcare resources.
In addition to validating clinical interventions, Cochrane's advocacy for RCTs
has contributed to the advancement of evidence-based medicine (EBM). EBM
emphasizes the integration of the best available research evidence with clinical
expertise and patient values to inform clinical decision-making. RCTs provide the
robust evidence needed to support clinical guidelines, inform treatment protocols, and
improve patient outcomes across various medical specialties.
Furthermore, Cochrane's legacy extends beyond the advocacy for RCTs to
encompass the establishment of systematic reviews as a critical tool for synthesizing
and evaluating medical evidence. Systematic reviews, pioneered by the Cochrane
Collaboration, involve a rigorous process of identifying, appraising, and synthesizing
all relevant studies on a particular topic to provide a comprehensive summary of the
evidence. By synthesizing data from multiple RCTs and other study designs,
systematic reviews offer clinicians and policymakers a reliable source of evidence to
inform healthcare decisions and policy development.
The impact of Cochrane's vision and contributions can be seen in the
widespread adoption of evidence-based practice across healthcare systems worldwide.
Clinicians, researchers, and policymakers increasingly rely on RCTs and systematic
reviews to guide clinical practice, develop treatment guidelines, and allocate
healthcare resources effectively. This evidence-based approach not only improves
patient outcomes but also enhances healthcare quality, promotes patient safety, and
fosters transparency in healthcare decision-making.
Moreover, Cochrane's influence has spurred advancements in research
methodology and ethics related to clinical trials. Efforts to enhance the design,
conduct, and reporting of RCTs have led to improvements in trial transparency,
patient recruitment, and ethical oversight. Initiatives such as the CONSORT
(Consolidated Standards of Reporting Trials) guidelines aim to standardize the
reporting of RCTs, ensuring that study results are transparent, reproducible, and
accessible to the broader healthcare community.
Looking forward, the legacy of Cochrane continues to inspire ongoing efforts
to expand the evidence base for clinical practice and improve the rigor and relevance
of clinical research. Innovations in trial design, including adaptive trials and
pragmatic trials, aim to address challenges such as participant recruitment,
generalizability of findings, and efficiency in resource utilization. These
advancements build upon Cochrane's foundational principles of scientific rigor,
evidence synthesis, and the promotion of patient-centered care.
In conclusion, Cochrane's advocacy for the randomized clinical trial as a
means of validating clinical interventions has profoundly shaped modern healthcare
practices. His commitment to scientific rigor, evidence-based medicine, and the
rational use of healthcare resources continues to guide clinicians, researchers, and
policymakers in their efforts to improve patient care, enhance healthcare quality, and
advance medical knowledge. By emphasizing the importance of evidence in
healthcare decision-making, Cochrane has left a lasting legacy that continues to
impact healthcare practice and policy worldwide.
Cochrane’s colleague, Iain Chalmers, began compiling a comprehensive
clinical trials registry of 3,500 clinical trial results in the field of perinatal medicine.
In 1988, after being published in print 3 years earlier, the registry became available
electronically. Chalmers’s methods for compiling the trials databases became a model
for future registry assembly. Eventually, the National Health Service in the United
Kingdom, recognizing the value of and need for systemic reviews for all of health
care, developed the Cochrane Center. The Cochrane Collaboration was initiated in
1993 and expanded internationally to maintain systematic reviews in all areas of
health care.
The Cochrane Collaboration database is an invaluable resource for healthcare
professionals and researchers, providing a comprehensive collection of high-quality,
evidence-based information that is essential for informed decision-making in
healthcare. By making this information easily accessible to students, faculty, and
nurses who work for university hospital systems, the Cochrane Collaboration
significantly enhances the educational and clinical environments within these
institutions.
The Cochrane Collaboration, an international network of researchers, health
professionals, and patients, is dedicated to producing systematic reviews of primary
research in human health care and health policy. These reviews are recognized for
their rigorous methodology and are considered the gold standard for determining the
effectiveness of healthcare interventions. The database includes thousands of
systematic reviews that cover a wide range of medical and health-related topics, from
treatment efficacy and diagnostic accuracy to the impact of preventive measures and
public health interventions.
For students, access to the Cochrane Collaboration database provides an
excellent foundation for their education and training. Nursing and medical students
can use the database to support their coursework, research projects, and clinical
decision-making. By engaging with the latest evidence-based research, students
develop critical appraisal skills and learn to apply scientific findings to real-world
clinical scenarios. This exposure to high-quality research early in their education
helps cultivate a culture of evidence-based practice, ensuring that future healthcare
professionals are well-equipped to deliver the best possible care to their patients.
Faculty members also greatly benefit from easy access to the Cochrane
Collaboration database. Educators can incorporate the latest evidence-based findings
into their teaching materials, ensuring that their curricula reflect current best practices
and advancements in healthcare. This integration of up-to-date research into
educational programs not only enhances the learning experience for students but also
supports faculty in staying current with emerging trends and developments in their
fields. Moreover, faculty members engaged in research can utilize the Cochrane
database to inform their studies, identify gaps in existing knowledge, and design
interventions that are grounded in the best available evidence.
Nurses working in university hospital systems are on the front lines of patient
care and greatly benefit from the Cochrane Collaboration database. By having ready
access to the latest evidence-based research, nurses can make more informed clinical
decisions, leading to improved patient outcomes. The database serves as a critical
resource for answering clinical questions, developing care plans, and implementing
evidence-based interventions. For example, a nurse dealing with a complex patient
case can quickly search the Cochrane database to find relevant systematic reviews that
provide guidance on the most effective treatments or management strategies. This
access to high-quality information supports clinical excellence and enhances the
quality of care delivered to patients.
Additionally, the Cochrane Collaboration database plays a crucial role in
fostering a culture of continuous learning and professional development among
nurses. By regularly consulting the database, nurses stay updated with the latest
research and advancements in healthcare, which is essential for maintaining clinical
competency and providing high-quality care. The database also supports nursing
leadership and quality improvement initiatives within university hospital systems.
Nursing leaders can use the evidence from Cochrane reviews to develop and
implement policies and protocols that are based on the best available research,
promoting standardization and improving patient safety.
The accessibility of the Cochrane Collaboration database is further enhanced
by the use of modern information technology. University hospital systems often
integrate the database into their electronic health record (EHR) systems and other
clinical information systems, making it easily accessible at the point of care. This
seamless integration ensures that healthcare providers can quickly access relevant
research without disrupting their workflow, facilitating timely and informed decision-
making.
Furthermore, the Cochrane Collaboration continuously updates its database to
reflect new research findings and emerging evidence. This commitment to providing
the most current information ensures that healthcare professionals have access to the
latest and most reliable data. The database is also designed to be user-friendly, with
powerful search functionalities and organized content that allows users to quickly find
the information they need. This ease of use is particularly important for busy
healthcare professionals who need to make rapid, evidence-based decisions.
In summary, the Cochrane Collaboration database is a vital resource that
significantly benefits students, faculty, and nurses working in university hospital
systems. By making high-quality, evidence-based information easily accessible, the
database supports education, research, and clinical practice. Students gain a solid
foundation in evidence-based practice, faculty can enhance their teaching and
research, and nurses can improve patient care and outcomes. The integration of the
Cochrane database into clinical information systems further facilitates access and
supports the delivery of high-quality, evidence-based healthcare. As the healthcare
landscape continues to evolve, the importance of resources like the Cochrane
Collaboration database in supporting informed decision-making and advancing
healthcare practice cannot be overstated.
g. Bioinformatics, Biomedical Informatics, and Computational Biology Defined
Three terms are frequently used: (1) bioinformatics, (2) biomedical
informatics, and (3) computational biology. As terms continue to be bandied about, it
is important to comprehend what they mean to understand better these evolving fields.
According to the University of Texas at El Paso (2010) website, “Bioinformatics is an
interdisciplinary science with a focus on data management and interpretation for
complex biological phenomena that are analyzed and visualized using mathematical
modeling and numerical methodologies with predictive algorithms” (para. 1). A
website operated by the University of Minnesota (2012) stated: Bioinformatics is
defined here as an interdisciplinary research area that applies computer and
information science to solve biological problems. However, this is not the only
definition. The field is being defined (and redefined) at present, and there are probably
as many definitions as there are bioinformaticians.
The myriad of definitions for the “moving target named bioinformatics”
(University of Minnesota, 2012, para. 2) is reflected in those developed from 2000 to
2009. According to NCBI (2004), “Bioinformatics is the field of science in which
biology, computer science, and information technology merge to form a single
discipline. The ultimate goal of the field is to enable the discovery of new biological
insights” (para. 5). Network Science (2009) believed that [a]n absolute definition of
bioinformatics has not been agreed upon. The first level, however, can be defined as
the design and application of methods for the collection, organization, indexing,
storage, and analysis of biological sequences (both nucleic acids [DNA and RNA] and
proteins). The next stage of bioinformatics is the derivation of knowledge concerning
the pathways, functions, and interactions of these genes (functional genomics) and
proteins (proteomics).
The National Human Genome Research Institute (NHGRI; 2015) describes
bioinformatics as a branch of biology concerned with the acquisition, storage, display,
and analysis of the information found in nucleic acid and protein sequence data.
Computers and bioinformatics software are the tools of the trade. Based on these
definitions, one can get a flavor for what bioinformatics entails. It is clear that the
definition of bioinformatics varies, and that there is no single definition that everyone
agrees with at the present time.
Biomedicine applies bioinformatics to promote health. According to the
website of the Ohio State University Medical Center (2010), “Biomedical informatics
is the study and process of efficiently gathering, storing, managing, retrieving,
analyzing, communicating, sharing, and applying biomedical information to improve
the detection, prevention, and treatment of disease”.
The Vanderbilt University (2016) website suggested that Biomedical
Informatics is the interdisciplinary science of acquiring, structuring, analyzing and
providing access to biomedical data, information and knowledge. As an academic
discipline, biomedical informatics is grounded in the principles of computer science,
information science, cognitive science, social science, and engineering, as well as the
clinical and basic biological sciences.
Oregon Health & Science University (OHSU; 2016) defined biomedical
informatics as “the field that is concerned with the optimal use of information, often
aided by the use of technology, to improve individual health, health care, public
health, and biomedical research” (para. 1). Bioinformatics can be viewed as a
biological science or computer science. According to the Bioinformatics Organization
(2011), bioinformatics as a biological science can be defined as “any use of computers
to characterize the molecular components of living things” (para. 2). As a computer
science, bioinformatics is the use of “computers to store, retrieve, analyze or predict
the composition or the structure of biomolecules”
Biomedical informatics is a growing field, with significant applications and
implications throughout the biomedical and clinical worlds. The authors believe that
biomedical informatics is the application of bioinformatics to health care.
Computational biology is the action complement of bioinformatics and, therefore,
biomedicine. NCBI (2004) stated: Ultimately, however, all of this information must be
combined to form a comprehensive picture of normal cellular activities so that
researchers may study how these activities are altered in different disease states.
Therefore, the field of bioinformatics has evolved such that the most pressing task
now involves the analysis and interpretation of various types of data, including
nucleotide and amino acid sequences, protein domains, and protein structures. The
actual process of analyzing and interpreting data is referred to as computational
biology.
The development and implementation of tools that enable efficient access to,
and use and management of, various types of information The development of new
algorithms (mathematical formulas) and statistics with which to assess relationships
among members of large data sets, such as methods to locate a gene within a
sequence, predict protein structure and/or function, and cluster protein sequences into
families of related sequences.
In 2000, the National Institutes of Health’s (NIH) Biomedical Information
Science and Technology Initiative Consortium defined bioinformatics and
computational biology. Members of the consortium believed that no definition could
completely eliminate overlap with other activities or preclude variations in
interpretation by different individuals and organizations. Ultimately, they defined
bioinformatics as the research, development, or application of computational tools and
approaches for expanding the use of biological, medical, behavioral or health data,
including those to acquire, store, organize, archive, analyze, or visualize such data.
The same group defined computational biology as the development and application of
dataanalytical and theoretical methods, mathematical modeling and computational
simulation techniques to the study of biological, behavioral, and social systems.
The Biomedical Information Science and Technology Initiative (BISTI) serves
as a pivotal program within the National Institutes of Health (NIH), focusing on
advancing biomedical computing and informatics to address complex challenges in
biomedical research and healthcare. BISTI plays a crucial role in fostering
collaboration, innovation, and the application of cutting-edge technologies across
various domains within the biomedical sciences.
At its core, BISTI aims to harness the power of information science,
computational biology, and technology to accelerate scientific discovery, improve
healthcare outcomes, and promote data-driven decision-making. The initiative
supports a wide range of research activities and infrastructure development aimed at
enhancing data integration, analysis, and dissemination across the biomedical research
community.
One of the key objectives of BISTI is to facilitate interdisciplinary
collaboration among researchers, clinicians, and computational scientists. By bringing
together experts from diverse fields such as biology, medicine, computer science,
statistics, and engineering, BISTI promotes cross-disciplinary research initiatives that
leverage complementary expertise and methodologies. This collaborative approach
fosters innovation and accelerates the translation of research findings into clinical
applications and public health strategies.
Furthermore, BISTI serves as a catalyst for developing and implementing
state-of-the-art computational tools and resources that support biomedical research.
These tools include advanced bioinformatics algorithms, machine learning models,
data visualization platforms, and high-performance computing infrastructures. By
providing researchers with access to these resources, BISTI enables them to analyze
large-scale datasets, conduct complex simulations, and generate actionable insights
that enhance our understanding of human health and disease.
BISTI also prioritizes the development of data standards, interoperability
frameworks, and data sharing policies that promote open science and collaboration.
By establishing common data formats and protocols, BISTI facilitates the integration
and exchange of biomedical data across research institutions, consortia, and
international networks. This interoperability is essential for maximizing the utility of
data resources, fostering reproducibility, and enabling secondary analyses that
uncover new research insights.
In addition to its research-focused initiatives, BISTI plays a critical role in
training the next generation of biomedical informaticians, computational biologists,
and data scientists. The initiative supports educational programs, workshops, and
training grants that equip researchers with the necessary skills and knowledge to
effectively leverage computational and informatics tools in their research endeavors.
By investing in workforce development, BISTI ensures a pipeline of skilled
professionals who can address emerging challenges and opportunities in biomedical
computing.
Moreover, BISTI engages in strategic partnerships with academic institutions,
industry partners, and other federal agencies to leverage resources, share expertise,
and accelerate technological advancements in biomedical informatics. Collaborative
initiatives fostered by BISTI include joint research projects, technology transfer
agreements, and consortia aimed at tackling specific biomedical research priorities,
such as precision medicine, biomedical imaging, drug discovery, and health
informatics.
Looking ahead, BISTI continues to evolve in response to emerging trends and
challenges in biomedical computing and informatics. The initiative remains
committed to advancing data-driven approaches to biomedical research, enhancing
computational capabilities, and promoting the integration of cutting-edge technologies
into clinical practice and public health initiatives. By supporting innovative research,
fostering collaboration, and training the next generation of leaders in biomedical
informatics, BISTI plays a vital role in shaping the future of biomedical science and
technology at the NIH and beyond.
In summary, the Biomedical Information Science and Technology Initiative
(BISTI) serves as a cornerstone of biomedical computing efforts at the NIH, driving
innovation, collaboration, and technological advancement across the biomedical
research landscape. Through its multifaceted approach to research support,
infrastructure development, training, and partnerships, BISTI continues to make
significant contributions to biomedical informatics, advancing our understanding of
health and disease and paving the way for transformative discoveries in healthcare.
Bioinformatics tools help biomedical informaticists and healthcare personnel
tackle the analysis of large datasets. The authors believe that biomedical informatics
uses bioinformatics, whereas computational biology is its action complement. By
using bioinformatics and computational biology to analyze and interpret intricate
biological events, biomedical informaticists promote health and improve patient care.
Biomedical informatics and bioinformatics may seem similar but if one thinks of
biomedical informatics as focusing on health care and patients, it helps distinguish
between the two.
Biomedical informaticists play a crucial role at the intersection of biology,
medicine, and information technology, utilizing bioinformatics methods to manage,
analyze, and interpret vast amounts of biological and medical data. Their work is
instrumental in advancing our understanding of the human body's complex biological
processes and mechanisms of disease, ultimately aiming to enhance health outcomes
through targeted interventions and personalized medicine.
At the core of biomedical informatics is the integration of diverse datasets,
ranging from genomics and proteomics to clinical records and imaging data. By
integrating these datasets, informaticists can uncover correlations, patterns, and
insights that are not readily apparent from individual datasets alone. This holistic
approach enables researchers and clinicians to gain a comprehensive view of
biological systems, disease mechanisms, and therapeutic targets.
Bioinformatics methods employed by biomedical informaticists encompass a
broad spectrum of computational techniques and tools. These include sequence
analysis algorithms for genomic data, protein structure prediction models, molecular
modeling simulations, and network analysis approaches to study interactions within
biological systems. High-throughput technologies such as next-generation sequencing
and mass spectrometry generate immense amounts of data, and informaticists develop
bioinformatics pipelines and algorithms to process, analyze, and extract meaningful
information from these datasets.
One significant area of focus for biomedical informatics is the advancement of
precision medicine. Precision medicine aims to tailor medical treatment and
prevention strategies to individual variations in genes, environment, and lifestyle.
Biomedical informaticists contribute to precision medicine initiatives by integrating
genomic data with clinical information to identify biomarkers, predict disease risk,
and optimize treatment protocols. This personalized approach holds promise for
improving treatment outcomes, minimizing adverse effects, and reducing healthcare
costs.
Furthermore, biomedical informaticists play a pivotal role in translational
research, bridging the gap between basic science discoveries and clinical applications.
By integrating biomedical data across different scales – from molecules and cells to
tissues and whole organisms – informaticists facilitate the identification of disease
mechanisms and the development of novel therapeutics. This interdisciplinary
approach accelerates the translation of research findings into clinical practice,
benefiting patients by bringing innovative treatments and diagnostic tools to the
bedside.
In addition to their contributions to research and clinical practice, biomedical
informaticists are instrumental in advancing public health initiatives and healthcare
policy. They analyze population-level data to identify trends, patterns of disease
spread, and risk factors, informing public health interventions and policy decisions.
Informaticists also play a critical role in healthcare quality improvement efforts by
developing data-driven methodologies for monitoring outcomes, assessing healthcare
delivery systems, and optimizing resource allocation.
The field of biomedical informatics is characterized by its collaborative
nature, involving partnerships among researchers, clinicians, data scientists, and
industry stakeholders. Collaborative initiatives enable informaticists to leverage
diverse expertise and resources, fostering innovation and accelerating scientific
discovery. These partnerships extend beyond traditional academic boundaries to
include collaborations with pharmaceutical companies, technology firms, government
agencies, and healthcare providers, creating synergies that drive advancements in
biomedical research and healthcare delivery.
Looking ahead, the role of biomedical informaticists is poised to expand with
advances in technology and data science. Emerging technologies such as artificial
intelligence (AI), machine learning, and deep learning hold promise for transforming
biomedical informatics by enabling predictive modeling, image analysis, and data-
driven decision support systems. These technologies have the potential to
revolutionize diagnostics, drug discovery, and patient care, paving the way for more
personalized and effective healthcare solutions.
In summary, biomedical informaticists leverage bioinformatics methods to
integrate and analyze large biological and medical datasets, aiming to enhance our
understanding of the human body and combat disease. Their work spans across
research, clinical practice, precision medicine, public health, and healthcare policy,
contributing to advancements in personalized medicine, translational research, and
healthcare delivery. By harnessing the power of data and technology, biomedical
informaticists play a pivotal role in driving innovation, improving health outcomes,
and shaping the future of biomedical science and medicine.
h. Why Are Bioinformatics and Biomedical Informatics So Important?
The future of health care is based on genomics. Bioinformatics and
computational biology have provided the tools to make it possible to analyze and
interpret complex biological processes. Through these developments, several projects
have advanced understanding of the human genome, haplotypes, and the genomic
changes related to disease.
This international partnership of scientists has taken blood samples from
clusters of related people, such as parents and children, from different international
regions. Using these samples, the researchers have been able to catalog some of the
common variations in DNA and investigate inherited alleles. As the name implies, a
haplotype map identifies a set of closely linked alleles on a chromosome that tend to
be inherited together.
According to NHGRI (2015), “One of the most important aspects of
bioinformatics is identifying genes within a long DNA sequence” (para. 1). It was
clear that the speed of DNA sequencing would have to be realized sooner in order to
decrease costs. The process was refined so the sequencing was improved. It took 4
years to sequence the first billion bases but just 4 months to sequence the second
billion bases.
During the month of January 2003, 1.5 billion bases were sequenced. As the
speed of DNA sequencing increased, the cost decreased from $10 per base in 1990 to
$0.10 per base at the conclusion of the project in April 2003 (NHGRI, 2015). One of
the most important aspects of bioinformatics is identifying genes within a long DNA
sequence. Until the development of bioinformatics, the only way to locate genes along
the chromosome was to study their function in the organism (in vivo) or to isolate the
DNA and study it in a test tube (in vitro). Bioinformatics allows scientists to make
educated guesses about where genes are located simply by analyzing sequence data
using a computer (in silico).
The other major piece brought out through the HGP was the realization that
ESLI arise from studying human genomes. The participants in the HGP set aside a
percentage of their annual budgets to research ESLI (HGP, 2008). Even though the
HGP has ended, researchers continue to improve DNA sequencing. Specifically, they
continue to advance the bioinformatics and computational biology tools that are used
in biomedical informatics. However, as Butte (2008) laments, “There is an absolute
paucity of people trained to make use of these resources, to build the infrastructure, to
ask these novel questions, and to even answer these questions”
These three projects were pivotal in genomics. The HGP focused on the DNA
sequence from a single individual, the HapMap project focused on variation in the
genome and on human populations, and the Cancer Genome Atlas Project is
concerned with how cancer affects the genomes. As a result of these seminal projects
and a unique culture of data sharing previously unknown among biological
researchers, molecular data and measurement tools are now publicly available. Two
examples of publicly available databases are the Gene Expression Omnibus, which is
maintained by the National Center for Biotechnology Information at the National
Library of Medicine, and Array-Express, which is maintained by the European
Bioinformatics Institute. As new researchers with both biology and computational
expertise emerge, bioinformatics and computational biology projects will contribute
new insights into disease mechanisms and therapeutic interventions.
