Question

Personalized or Precision Medicine Presentation

The purpose of this assignment is to examine the importance of informatics and evolving trends in personalized or precision medicine.

As a health informaticist, create a 10-slide presentation to your clinical staff discussing the following:

1. Discuss the role of personalized or precision medicine and the advantages and disadvantages of personalized or precision medicine in the delivery of health care.
2. Discuss how the fields of personalized or precision medicine and informatics have mutually affected one another.
3. Discuss the importance of creating personalized or precision medicine applications that collect accurate data and how those data are communicated to various locations and providers.
4. Describe how personalized or precision medicine supports the aging population and population health.
5. Describe current gaps and challenges in personalized or precision medicine and informatics. How could the future of health informatics evolve over time to respond to those gaps?

Integrate four or five scholarly articles in your paper.

Answer 1

Precision medicine is a way health care providers can offer and plan specific care for their patients, based on the person's genes. It's sometimes called personalised medicine or personalised care.

Personalised medicine is a term used for the treatment focusing on the patients based on their individual clinical characterization, considering the diversity of symptoms, severity, and genetic traits.

Understanding individual factors can help us treat disease more effectively or even prevent diseases more altogether. This method of tailoring treatment to an individual based on specific personalized factors is called Precision Medicine.

* Some of the potential advantages of precision medicine are as follows -

• The efficiency of care -

Precision medicine makes decisions based on individual specific factors that affect their health. Today, decision making regarding treatments is on the shoulders of the patients, as even doctors do not know how certain treatment will affect a particular individual. With precision, medical providers can cater customized treatment methodology for each of their patients, improving the probability of recovery.

• Preventive care -

When the genetic screening process collects enough samples, the results can be used to diagnose genetically caused diseases and even prevent such diseases by understanding the risk of an individual rather than simply reacting to an illness. The presence or absence of some genes can cause diseases, and by studying these variations we can protect ourselves from these diseases.

• Limit costs -

Targeted treatment on the basis of genetic mapping can reduce the cost of care with more informed treatment decisions and a greater chance of being effective. The cost will be potentially lower with the focus on preventive care rather than treatment of disease.

• Population health -

Studying genetic patterns in a population as a whole, and as sections can help in identifying causes for particular diseases and develop the treatment. Genetic study of sections of a population can predict a likelihood of diseases and early detection.

* Some of the disadvantages of precision medicine are as follows -

• Infrastructure requirement -

Precision medicine has the potential to deeply impact healthcare, but for that, it requires massive infrastructure investments and time to implement. To implement precision medicine, fundamental changes must be made to infrastructure and mechanism of data collection, storage, and sharing. The federal fund earmarked for the development of precision medicine will not cover the requirement and the question of who will have to spend the rest of the fund is unclear.

• Legal problems -

For Precision Medicine to reach peak efficiency, a lot of genomic data must be collected from a large and diverse section of the population. If and when such a massive amount of data is collected, it is legally unclear who owns the data. The government does not own the data and the FDA has blocked individuals from accessing their own genetic information from companies. In addition to legal issues, the collection and storage of such large amounts of data bring privacy into question. As a result, the implementation of such a plan would definitely be met with significant pushback.

• The relevance of the information -

The possibility of ‘missing out’ on certain sections of the population or inadequate samples of certain disorders or over-representation of other types of disorder is highly likely.

• Healthcare cost -

Ideally, precision medicine can eliminate repeated efforts, readmission and help take preventive measures against disease, stopping the hemorrhage of funds in healthcare. But to reach this stage, it requires massive investment in infrastructure for collecting, storing, and sharing this information.Investment must also be made to security infrastructure to protect the data and other add-on expenses could prove to be a burden.

##

Precision medicine applies informatics to its big data collection and analysis efforts by putting things in perspective. Providing data that has clinical relevance on a patient specific level as well as presenting that data in a way that makes it easier for physicians to interpret and use it in their decision-making process, informatics is essentially the engine that will power precision medicine into widespread adoption.

Clinical informatics and biomedical informatics,with their focus on how information is collected, stored, analyzed and disseminated, are two important areas for the advancement of precision medicine.

* Providing data that has clinical relevance on a patient specific level as well as presenting that data in a way that makes it easier for physicians to interpret and use it in their decision-making process, informatics is essentially the engine that will power precision medicine into widespread adoption.

## With the ever-increasing data needs to obtain patient level analysis, simply collecting and aggregating the data is not enough. With the rise in patient specific treatments, manufacturers will require a closer and more intimate knowledge of the patient and their journey. The focus and effort put into data aggregation and collection should always keep in mind the overall objective, which is to facilitate the full journey from patient identification and product administration to monitoring patient outcomes.

Access to fine-grained health data is the scalability challenge for personalized medicine. New evidence derived from large populations of data is needed to direct the development of new targeted drugs, to discover unmet needs and the value of novel therapies. An evidence-based reorganization of healthcare is also needed to deliver more personalized packages of care.

Infrastructures providing large populations of detailed health records, alongside genotypic information and/or bio-samples, are a critical success factor to scaling up personalized medicine Biobanks, as a high-fidelity resource of health data and samples. Most challenging is how to conduct research on large networks of routinely collected health data on the broad range of patients who reflect the true diversity of persons with a condition and not only those who volunteer for clinical trials or biobanks. Some countries have established multiple patient registries that collect and clean routine EHR data for use in healthcare quality improvement and in research, perhaps in Europe the most well-known being Sweden.

an overview of personalized medicine, offering perspectives in four important, influencing topics: 1) the availability of “big data” and the role of biomedical informatics in personalized medicine, 2) the need for interdisciplinary teams in the development and evaluation of personalized therapeutic approaches, 3) the impact of electronic medical record systems and clinical data warehouses on the field of personalized medicine, and 4) strategies that will increase the impact of personalized medicine as a tool to promote health equity. Ongoing challenges to the translation of biomedical information into applied clinical practice of personalized medicine includes reclassification of disease states, scientific challenges such as determining which genetic markers have the most clinical significance, and policy regulation of genetic testing. Health informaticians are critical partners in overcoming these challenges, working with clinicians and researchers, to identify and implement data analytics, including the development of predictive data mining strategies, to enable the use of clinical data repositories for personalized therapies. Some have expressed concerns that personalized medicine may more significantly benefit those patients who have greater access to care, further deepening already existing health disparities and health inequities, However, there is a significant opportunity for personalized medicine to increase treatment accuracy and decrease social and scientific bias.

## The ageing population is associated with an increase in multimorbidity, chronic diseases, and disability, which render it frail and vulnerable, and results in increased health care costs. Identifying ways for optimizing therapeutic interventions, thus reducing prescriptions and minimizing harm in this population, is becoming a priority in order to develop decision-making processes that classify patients according to their risk. Indeed, health risk assessment models are recommended preventive care interventions, which are capable of implementing disease prevention and health promotion in older individuals.

In this light, the possibility of creating algorithms that include the patient’s age, co-morbidities, and polypharmacy may enhance the sensitivity of existing available biomarkers and allow the discovery of new, more specific, ones, and enable the development of appropriate testing for this particular cluster of patients.

Technology is reshaping the way we think of health and will transform current medical and public health practice. The economic impact will be substantive, providing a roadmap for reining in healthcare expenditures and improving health status. The therapeutic arena is already experiencing affirmative outcomes through improved technology-based interventions, with accelerated expansion made possible through the emergence of prognostic digital biomarkers. Capturing sensitive disease-related measures will facilitate early action that could mitigate or modify the course of disease whose heterogeneous expression have made effective treatments to date fleeting. Clinical studies and animal models should be further validated so that a wider range of interventions can be available for individuals to apply a combinatory approach for health promotion. With greater personalization of healthcare in all stages of the life course, the public health perspective is shifting from precision medicine to precision health. Furthermore, complementary to the medical model is an emergent new business ecosystem comprised of tens of thousands if not more new technology companies centered on providing the right solution to the right person at the right time. Given the global pandemic of chronic diseases, the challenge now is to bring together these key stakeholders to constrict the timeline of reaching the precision health objective. Interdisciplinary contributions that are harmonized and broadly shared are central to finding real solutions that will drive down healthcare costs and increase health quality.

## The gaps between research and applied genomic medicine may be a result of a cultural gap across various stakeholders representing scientists, clinicians, patients, policy makers, and third party payers. Scientists are trained to assess the health care value of genomics by either quantifying population-scale effects, or through the narrow lens of clinical trials where the standard of care is compared with the predictive power of a single or handful of genetic variants. While these metrics are an essential first step in assessing and documenting the clinical utility of genomics, they are rarely followed up with other assessments of health care value that are critical to stakeholders who use different measures to define value. The limited value assessment in both the research and implementation science of precision medicine is likely due to necessary logistical constraints of these teams; engaging bioethicists, health care economists, and individual patient belief systems is incredibly daunting for geneticists and informaticians conducting research. In this narrative review, we concisely describe several definitions of value through various stakeholder viewpoints. the existing gaps that prevent clinical translation of scientific findings generally as well as more specifically using two present-day, extreme scenarios: (1) genetically guided warfarin dosing representing a handful of genetic markers and more than 10 years of basic and translational research, and (2) next-generation sequencing representing genome-dense data lacking substantial evidence for implementation. These contemporary scenarios highlight the need for various stakeholders to broadly adopt frameworks designed to define and collect multiple value measures across different disciplines to ultimately impact more universal acceptance of and reimbursement for genomic medicine.

The biggest gap for clinical teams is the lack of access to the data. We know that EHRs are not known for being able to consume biomarker results in a structured way. Instead, biomarker results are predominantly generated from an external entity that’s not part of the network of a hospital system. These results are often received as a PDF report that contains unstructured data. To transform this data into a structured format that is readily accessible and searchable in the future is often a time consuming, labor-intensive manual process. Leveraging a system that can ingest, compute, store, and display biomarker data in a meaningful, accessible way, would be a huge benefit.
Another gap in current HIT systems that
would provide benefit to users and ultimately to the patients is the lack of collaboration tools such as a virtual molecular tumor board. Precision medicine is all about collaboration around genomic and other biomarker results and being able to understand how to use test results. The best approach is to, first, find ways to get the information into the EHR and second, to provide a framework for collaboration with peers and other subject matter experts that can help guide therapy decisions.