Clinical labs serve as medical detectives who discover answers to healthcare puzzles. According to estimates, their data impacts 70 percent of physician treatment decisions.
Labs provide immense value in helping communities identify risks and care gaps within populations, as well as take proactive approaches towards improving health outcomes.
1. Detection of Diseases
Medical laboratories provide invaluable information for diagnosis, monitoring of treatment progress and patient health. However, test results must be interpreted with an understanding of both their strengths and limitations as well as within their context within an individual patient’s health history.
Medical laboratory science (MLS) is an interdisciplinary field which utilizes chemical, hematological, immunological and microscopic analyses on blood samples from patients in order to aid their overall diagnosis. Physicians and other health care professionals rely on this data for decision making purposes.
Hospitals, clinics and private practices typically feature these laboratories that combine anatomic pathology (the assembly and analysis of tissue samples for diagnostic purposes) with clinical pathology (diagnosing disease using blood and other body fluids).
Many clinical laboratories are engaging in research at multiple levels, primarily by creating new testing approaches to address specific research questions and creating future integration opportunities into regular patient care services. They can also support protocol design by providing advice about which tests would best answer each research query; this process is made simpler by creating clinical laboratory networks which facilitate collaboration among laboratory employees for these activities.
2. Monitoring of Patients
Central laboratory service serve numerous vital roles to assist physicians and clinicians in providing care to their patients. From simple blood tests to cutting-edge genetic and molecular analyses, clinical laboratories offer vital insight into a person’s condition that aids health care providers when making decisions about screening, diagnosing, monitoring, treating, or managing disease.
No matter the technical sophistication of modern laboratory instruments, their accuracy and precision rely solely on their operators: laboratory scientists. Lab professionals ensure testing procedures are followed in an organized and precise manner. Clinical laboratories perform routine laboratory tests as well as specialty immunoassay tests that cannot easily be found on common analyzers for research protocols requiring complex systems like immunoassay test systems that support specific immunoassay testing procedures.
Clinical laboratories perform complex and expensive testing procedures for clinical research studies, often taking time and incurring substantial expenses. To help reduce costs and turnaround times, many have turned to Lean quality methodologies in their operations.
Future clinical studies may combine traditional translational medicine and digital measurements in innovative ways, with laboratories taking an active part in gathering the necessary data needed for developing and deploying digital measurement technologies – this will open up opportunities to reimagine how clinical studies reach and engage patients and healthcare providers alike.
3. Predictive Modeling
Predictive modeling is a form of data analysis that uses computer software and algorithms to predict potential future scenarios for entities from which data has been gathered. It can be applied in any industry, enterprise or endeavor where information is accumulated – whether that means making more accurate and informed decisions, increasing revenue streams, improving marketing campaigns or combatting fraud – predictive modeling offers great potential.
Data science involves collecting information, creating a statistical model to predict outcomes, validating or revising said model based on past and current events, then refining (or revising) it accordingly. Machine learning is one subset of artificial intelligence often referred to as data mining.
Predictive models have long been employed by health insurance providers to assess customers’ risk profiles and the likelihood that they’ll file an insurance claim, which enables them to set more accurate premiums and meet profit targets more precisely. Predictive models also enable banks and financial services industries to better understand customers and make more informed credit-rating and lending decisions using predictive modeling techniques.
Predictive modeling success requires the involvement of multiple people, starting with an executive sponsor to secure funding and prioritize the project, followed by a line-of-business manager who has a thorough knowledge of the problem to be solved and provides actionable insight; finally a data wrangler capable of collecting, prepping and integrating data for analysis is required to build the model.
4. Detection of Genetic Variations
As clinical laboratories transition towards value-based care, they must be capable of translating research findings into actionable insights for patients. To do this, they must expand their role in research by drawing upon their domain knowledge and offering customized services tailored specifically towards clinical needs.
One way of doing this is through clinical laboratories’ proximity to patient care centers and point-of-care (POC) testing; together these elements allow labs to improve healthcare quality by reducing unnecessary visits and providing timely results.
POC tests for infections like suspected pharyngitis can assist clinicians in quickly diagnosing and treating patients, decreasing unnecessary hospital admissions and prescription costs while mitigating co-occurring health conditions associated with prolonged hospital stays.
As another example, genomic testing can also be used to assess pharmacogenetics. Genetic variants affecting enzymes that break down or metabolize drugs can significantly change their therapeutic efficacy or cause adverse reactions; one common oral anticoagulant known as warfarin has a narrow therapeutic range and could even cause excessive bleeding when taken by patients who carry certain genetic variants.
Laboratory researchers must offer clinicians comprehensive, clear, and precise reports that detail the scope and sensitivity of tests (i.e., number of variants tested for) along with interpretation and recommendations. Orthogonal confirmation of NGS-detected germline variants may also help ensure accurate reports; although recent studies have questioned its necessity.
5. Detection of Infections
While doctors often suspect an infection based on symptoms and physical examination results, lab tests must be run to confirm it. They use blood and urine samples among other body fluids to identify bacterial, fungal, and viral infections in their patients.
Scientists use microscopes to inspect samples to see if there are any microorganisms present. Stains may also help make bacteria or fungi easier to spot from other cells in a sample. Once they know which germ is present, lab technicians can test how a patient’s immune system reacts by testing whether there are antibodies (immunological tests) or molecular analysis including polymerase chain reaction [PCR].
Once a lab has cultured microorganisms in an experiment, they can conduct antimicrobial drug tests against them to assess how effective certain antibiotics are against them – this information allows doctors to select the most appropriate treatments. Furthermore, genetic testing can detect any resistance genes present that make some germs resistant to certain antibiotics.
Clinical laboratories play a critical role in research, yet it’s crucial for them to maintain the distinction between research and routine service. Clinical laboratories should seek to develop expertise that complements research protocols while simultaneously meeting operational and economic pressures – something which will allow them to continue providing high-quality patient care leading to improved health outcomes for all.
6. Detection of Cancer
Global central laboratory services generate objective data using cutting-edge technologies that is of immense use in medical decision making. This information has direct bearing on patient diagnosis and serves as an integral component of protocols used by physicians and healthcare team members to prevent, diagnose and treat diseases.
When blood tests show elevated levels of certain substances, this could be an early warning sign of cancer. To assist doctors in making accurate diagnoses, clinical laboratory scientists conduct further testing of blood and body fluids to detect cancer presence; additionally they analyze biopsy samples for tumor markers released into blood as the tumor develops and spreads throughout its growth.
Clinical laboratories play an essential role in discovering new tools and methods that can be used to detect cancer. This may involve advanced genomic or molecular testing methods that identify mutations that lead to cancer development, helping oncologists create targeted drug therapies or immunotherapies that target specific parts of tumors while minimizing side effects.
Current efforts to develop cutting-edge cancer diagnostics are taking place in clinical laboratories. Researchers participating in the NCI Cancer MoonshotSM Biobank, for instance, are recruiting patients undergoing cancer treatments and collecting their tissue and blood samples in order to study how tumor markers may help improve diagnosis accuracy as well as identify new therapies across multiple cancer types.
