A strategic approach to chronic care management (CCM) is essential.
This approach should integrate advanced technology with a deep comprehension of healthcare’s fundamental principles, effectively tackling the current challenges and proactively shaping the future of healthcare.
Emphasizing patient-centered solutions, this methodology is set to revolutionize the management of chronic diseases, thereby impacting patient care and healthcare economics significantly.
This strategy’s critical element is distinguishing between care management (CM) and case management. Care management represents a holistic approach to reducing health risks and costs across populations. It involves identifying at-risk populations, tailoring services to their needs, and deploying appropriate personnel for service delivery.
Conversely, case management focuses more on individual patients, offering various services to aid in navigating the healthcare system, ensuring efficient care transitions, and addressing specific patient needs.
Integrating Population Health Management (PHM) and CCM systems offers a solution to managing chronic diseases. PHM systems, which adopt a community-centered approach, work in tandem with CCM systems that concentrate on individual patient care. This synergy allows for effective management of both community health and individual patient needs
A new diagnostic technique that has the potential to identify opioid-addicted patients at risk for relapse could lead to better treatment and outcomes.
Using an algorithm that looks for patterns in brain structure and functional connectivity, researchers were able to distinguish prescription opioid users from healthy participants. If treatment is successful, their brains will resemble the brain of someone not addicted to opioids.
“People can say one thing, but brain patterns do not lie,” says lead researcher Suchismita Ray, an associate professor in the health informatics department at Rutgers School of Health Professions.
“The brain patterns that the algorithm identified from brain volume and functional connectivity biomarkers from prescription opioid users hold great promise to improve over current diagnosis.”
In the study in NeuroImage: Clinical, Ray and her colleagues used MRIs to look at the brain structure and function in people diagnosed with prescription opioid use disorder who were seeking treatment compared to individuals with no history of using opioids.
The scans looked at the brain network believed to be responsible for drug cravings and compulsive drug use. At the completion of treatment, if this brain network remains unchanged, the patient needs more treatment.
Skeleton/bone marrow involvement in patients with newly diagnosed Hodgkin’s lymphoma (HL) is an important predictor of adverse outcomes1. Studies show that FDG-PET/CT upstages patients with uni- or multifocal skeleton/bone marrow uptake (BMU) when iliac crest bone marrow biopsy fails to find evidence of histology-proven involvement. The general recommendation is, therefore, that bone marrow biopsy can be avoided when FDG-PET/CT is performed at staging.
Our aim was to develop an AI-based method for the detection of focal skeleton/BMU and quantification of diffuse BMU in patients with HL undergoing staging with FDG-PET/CT. The output of the AI-based method in a separate test set was compared to the image interpretation of ten physicians from different hospitals. Finally, the AI-based quantification of diffuse BMU was compared to manual quantification.
Artificial intelligence-based classification
A convolutional neural network (CNN) was used to segment the skeletal anatomy11. Based on this CNN, the bone marrow was defined by excluding the edges from each individual bone; more precisely, 7 mm was excluded from the humeri and femora, 5 mm was excluded from the vertebrae and hip bones, and 3 mm was excluded from the remaining bones.
Focal skeleton/bone marrow uptake
The basic idea behind our approach is that the distribution of non-focal BMU has a light tail and most pixels will have an uptake reasonably close to the average. There will still be variations between different bones. Most importantly, we found that certain bones were much more likely to have diffuse BMU than others. Hence, we cannot use the same threshold for focal uptake in all bones. At the other end, treating each bone individually is too susceptible to noise. As a compromise, we chose to divide the bones into two groups:
“spine”—defined as the vertebrae, sacrum, and coccyx as well as regions in the hip bones within 50 mm from these locations, i.e., including the sacroiliac joints.
“other bones”—defined as the humeri, scapulae, clavicles, ribs, sternum, femora, and the remaining parts of the hip bones.
For each group, the focal standardized uptake values (SUVs) were quantified using the following steps:
Threshold computation. A threshold (THR) was computed using the mean and standard deviation (SD) of the SUV inside the bone marrow. The threshold was set to
���=SUVmean+2SD.
2. Abnormal bone region. The abnormal bone region was defined in the following way:
Only the pixels segmented as bone and where SUV > THR were considered. To reduce the issues of PET/CT misalignment and spill over, a watershed transform was used to assign each of these pixels to a local maximum in the PET image. If this maximum was outside the bone mask, the uptake was assumed to be leaking into the bone from other tissues and was removed. Finally, uptake regions smaller than 0.1 mL were removed.
3.Abnormal bone SUV quantification. The mean squared abnormal uptake (MSAU) was first calculated as
MSAU=meanof(SUV−THR)2overtheabnormalboneregion.
To quantify the abnormal uptake, we used the total squared abnormal uptake (TSAU), rather than the more common total lesion glycolysis (TLG). We believe TLG tends to overestimate the severity of larger regions with moderate uptake. TSAU will assign a much smaller value to such lesions, reflecting the uncertainty that is often associated with their classification. Instead, TSAU will give a larger weight to small lesions with very high uptake. This reflects both the higher certainty with respect to their classification and the severity typically associated to very high uptake.
TSAU=MSAU×(volumeoftheabnormalboneregion).
This calculation leads to two TSAU values; one for the “spine” and one for the “other bones”. As the TSAU value can be nonzero even for patients without focal uptake, cut-off values were tuned using the training cohort. The AI method was adjusted in the training group to have a positive predictive value of 65% and a negative predictive value of 98%. For the “spine”, a cut-off of 0.5 was used, and for the “other bones”, a cut-off of 3.0 was used. If one of the TSAU values was higher than the corresponding cut-off, the patient was considered to have focal uptake.
Results
Focal uptake
Fourteen of the 48 cases were classified as having focal skeleton/BMU by the AI-based method. The majority of physicians classified 7/48 cases as positive and 41/48 cases as negative for having focal skeleton/BMU. The majority of the physicians agreed with the AI method in 39 of the 48 cases. Six of the seven positive cases (86%) identified by the majority of physicians were identified as positive by the AI method, while the seventh was classified as negative by the AI method and by three of the ten physicians.
Thirty-three of the 41 negative cases (80%) identified by the majority of physicians were also classified as negative by the AI method. In seven of the remaining eight patients, 1–3 physicians (out of the ten total) classified the cases as having focal uptake, while in one of the eight cases none of the physicians classified it as having focal uptake. These findings indicate that the AI method has been developed towards high sensitivity, which is necessary to highlight suspicious uptake.
Conclusions
The present study demonstrates that an AI-based method can be developed to highlight suspicious focal skeleton/BMU in HL patients staged with FDG-PET/CT. This AI-based method can also objectively provide results regarding high versus low BMU by calculating the SUVmedian value in the whole spine marrow and the liver. Additionally, the study also demonstrated that inter-observer agreement regarding both focal and diffuse BMU is moderate among nuclear medicine physicians with varying levels of experience working at different hospitals. Finally, our results show that the automated method regarding diffuse BMU is comparable to the manual ROI method.
Two scientific leaps, in machine learning algorithms and powerful biological imaging and sequencing tools , are increasingly being combined to spur progress in understanding diseases and advance AI itself.
Cutting-edge, machine-learning techniques are increasingly being adapted and applied to biological data, including for COVID-19.
Recently, researchers reported using a new technique to figure out how genes are expressed in individual cells and how those cells interact in people who had died with Alzheimer's disease.
Machine-learning algorithms can also be used to compare the expression of genes in cells infected with SARS-CoV-2 to cells treated with thousands of different drugs in order to try to computationally predict drugs that might inhibit the virus.
While, Algorithmic results alone don't prove the drugs are potent enough to be clinically effective. But they can help identify future targets for antivirals or they could reveal a protein researchers didn't know was important for SARS-CoV-2, providing new insight on the biology of the virus
The insight in this article is shared among a number of early adopters and tinkerers in the Healthcare ML space. A number of specific problems which are being worked on within the Machine learning space which relate to life sciences are stimulants which help us advance the science of machine learning much faster than other areas.
This is because the science of Biology requires more than patterns being found and re-applied to identify something. It requires understanding the interaction of all the contributing factors behind that pattern being created in the first place. So, creating a drug to target a protein involved in a disease does require understanding how the genes that give rise to that protein are regulated.
Life sciences companies have too much information—manually collected, logged and stored to adhere to the highest quality standards. Information is forever coming from all different directions, including R&D, manufacturing, clinical trials and even patient care.
Digital analytics can funnel just the right information for risk management.
Big data analytics technology has been able to find patterns and pinpoint disease states more accurately than even the most highly-trained physicians.
The human brain may be nature’s finest computer, but artificial intelligences fed on big data are making a convincing challenge for the crown. In the realm of healthcare, natural language processing, associative intelligence, and machine learning are revolutionizing the way physicians make decisions and diagnose complex patients, significantly improving accuracy and catching deadly issues before symptoms even present themselves.
In this case study examining the impact of big data analytics on clinical decision making, Dr. Partho Sengupta, Director of Cardiac Ultrasound Research and Associate Professor of Medicine in Cardiology at the Mount Sinai Hospital, has used an associative memory engine from Saffron Technology to crunch enormous datasets for more accurate diagnoses.
Using 10,000 attributes collected from 90 metrics in six different locations of the heart, all produced by a single, one-second heartbeat, the analytics technology has been able to find patterns and pinpoint disease states more quickly and accurately than even the most highly-trained physicians.
As the number of self-tracking health and fitness tools available to consumers continues to climb, a persistent question has been whether the data they collect might be useful to health researchers. Along with that: Are people who self-track comfortable sharing their data with researchers?
A new, must-read report from San Diego’s California Institute for Telecommunications and Information Technology (Calit2), funded by the Robert Wood Johnson Foundation, explores these and other questions.
Based on a survey with hundreds of self-trackers, a majority — 57 percent — said one critical assurance they would need before agreeing to make their self-tracked, personal health data available to researchers was that their privacy would be protected. More than 90 percent also said it was important that their data remained anonymous. Respondents said they’d be more comfortable sharing data if they knew it was only going to be used for “public good” research.
One open-ended survey that the report’s researchers posed to self-trackers found that 13 percent of respondents specifically mentioned an aversion to commercial or profit-making use of their data, according to the report. One respondent wrote: “It depends who gets it. Research using these data will be instrumental in the future of personal predictive services, but also for that reason are likely to be exploited by marketers and the politically short-sighted. Thus I would like transparency for who has access to my data.”
Among the almost 100 health researchers interviewed for the report, 46 percent said that they had already used self-tracking data in their research previously. Some 23 percent reported that they had already worked with digital health companies that offer apps or devices to consumers to track their health.
Overall, the researchers interviewed for the report were “generally enthusiastic” about the prospect of using self-tracking data in the future — 89 percent agreed or strongly agreed that such data would prove useful to their research efforts. Almost all of those researchers surveyed said that kind of data could answer questions that other data could not.
A deeper look at five technologies that are currently advancing exponentially and radically reshaping healthcare. In other words, for the long suffering, there is plenty of hope to go around.
3-D printing
3D printing is already making its presence felt in medical device world. Ninety-five percent of all hearing aids are today 3D printed. This tech is also pushing into prosthetics. There are custom-made back braces for scoliosis patients and casts for broken bones (perforated with holes so people can finally scratch through their casts) and, in the latest development, 3D printed facial prosthetics (noses, ears, etc.).
Artificial Intelligence
It started with IBM’s Watson. After besting humans on Jeopardyback in 2011, Big Blue sent their thinking machine to medical school. Now loaded up with everything from journal articles to medical textbooks to actual information culled from patient interviews, the supercomputer has remerged as an incredibly robust diagnostic aid that is already being used for everything from training medical students to managing the treatment of lung cancer.
Brain- Computer Interfaces
We’ve been hearing about BCIs for a little while now. The tech originated out of the desire to help paraplegics and quadriplegics control computer cursors with only their brains. Of course, these developments will continue apace, bringing far more liberation to the disabled then ever before possible, but the bigger news is in BCIs that can control robotic limbs or even restore function to paralyzed limbs.
Robotics:
The robots are coming, the robots are coming, the robots are, well, here. Whether we’re talking the da Vinci Surgical System—which has performed over 20,000 operations since its 2000 debut—or newer developments like the nanobots swimming through our bloodstream and scraping plaque from our arteries, robots are already deep into the healthcare space.
Point-of-Care Diagnostics
In medicine, one of the major promises of technology is patient empowerment—especially when it comes to diagnostics. Suddenly, patients no longer have to go to the doctor’s office or hospital. Instead, in the comfort of your home, a system called the Tricorder will analyze data, diagnose the problem, and send that information to a doctor who, quite possibly, can treat you remotely. In the developed world, where doctors make diagnostic errors 10 percent of the time, this will make a significant difference in quality-of-care and significantly reduce the roughly $55 billion spent annually on the malpractice system) In the developing world, this will make healthcare far more accessible.
Re Point of Care: in the developed world, machines will make diagnostic errors a lot more often than physicians. And in the developing world, yes, access to care would improve but that doesn't address the other issue, which is paying for required treatment, whether pharmaceutical or otherwise. I'll go complain in the comments for the original post at Forbes, not here, as it isn't your fault! Thank you for sharing with us; I don't intend to seem ungrateful.
Monitoring social media websites like Twitter could help health officials and providers identify in real time severe medical outbreaks, allowing them to more efficiently direct resources and curb the spread of disease, according to a San Diego State University studypublished last month in the Journal of Medical Internet Research,Medical News Today reports.
Study Details
For the study, lead researcher and San Diego State University geography professor Ming-Hsiang Tsou and his team used a program to monitor tweets that originated within a 17-mile radius of 11 cities. The program recorded details of tweets containing the words "flu" or "influenza," including:
Origin;
Username;
Whether the tweet was an original or a retweet; and
Any links to websites in the tweet.
Researchers then compared their findings with regional data based on CDC's definition of influenza-like illness.
The program recorded data on 161,821 tweets that included the word "flu" and 6,174 tweets that included the word "influenza" between June 2012 and the beginning of December 2012.
According to the study, nine of the 11 cities exhibited a statistically significant correlation between an uptick in the number of tweets mentioning the keywords and regional outbreak reports. In five of the cities -- Denver, Fort Worth, Jacksonville, San Diego and Seattle -- the algorithm noted the outbreaks sooner than regional reports.
This is a cool article to show the real life change that social media is creating. Before it was stated that it would take up to two weeks to detect an outbreak of a disease but now with social media it can be done in a day.
This article really shows how social media is becoming a part of our everyday life and is taking on roles that we probably didn't expect it to.
However with the number of users increasing it is important to have tools that help us monitor the large amount of data that is present.
Its no good having all this information if we cannot harness it's true potential, like the one illustrated in this article for disease break out.
With the increasing burden of chronic diseases, analyzing and understanding trajectories of care is essential for efficient planning and fair allocation of resources: authors Nicolas Jay, Gilles Nuemi, Maryse Gadreau and Catherine Quantin propose an approach based on mining claim data to support the exploration of trajectories of care.
A clustering of trajectories of care for breast cancer was performed with Formal Concept Analysis. We exported Data from the French national casemix system, covering all inpatient admissions in the country. Patients admitted for breast cancer surgery in 2009 were selected and their trajectory of care was recomposed with all hospitalizations occuring within one year after surgery. The main diagnoses of hospitalizations were used to produce morbidity profiles. Cumulative hospital costs were computed for each profile.
Formal Concept Analysis can be applied on claim data to produce an automatic classification of care trajectories. This flexible approach takes advantages of routinely collected data and can be used to setup cost-of-illness studies.
Enabling bi-directional APIs is one way to offer speed, efficiency and security while preserving the most necessary components of human intervention. These closed-loop data retrieval processes will shape the future of ROI in healthcare for higher quality and faster intake and fulfillment.
Bi-directional APIs give healthcare providers maximum data visibility and control. Here’s how they’re shaping the future of release of information.
In recent years, a fire has been lit under healthcare’s use of application programming interfaces (APIs). Actually, it has been a FHIR [Fast Healthcare Interoperability Resources] as regulations encourage electronic medical record (EMR) vendors to continue to build its standards into their systems, expanding functionality and improving usability. But that does not mean the road to digitization and interoperability has been seamless, particularly as it relates to release of information (ROI).
More than 100 million HIV tests are performed around the world annually, meaning even a small improvement in quality assurance could impact the lives of millions of people by reducing the risk of false positives and negatives.
Academics from the London Center for Nanotechnology at UCL and AHRI used deep learning (artificial intelligence/AI) algorithms to improve health workers' ability to diagnose HIV using lateral flow tests in rural South Africa.
Their findings, published today in Nature Medicine, involve the first and largest study of field-acquired HIV test results, which have applied machine learning (AI) to help classify them as positive or negative.
By harnessing the potential of mobile phone sensors, cameras, processing power and data sharing capabilities, the team developed an app that can read test results from an image taken by end users on a mobile device. It may also be able to report results to public health systems for better data collection and ongoing care.
The use of mobile tools for data capture and AI/ML algorithms for diagnostics and detections has been the inside story of digital health over the past 4 years. This is an excellent study and shows the promise of this combination of technologies in building the future of healthcare. HIV is a pandemic which must be eradicated.
The Mayo Clinic has launched a new mHealth platform aimed at helping healthcare providers improve their use of connected health devices in remote patient monitoring and other mobile health programs.
The Remote Diagnostic and Management Platform (RDMP) connects devices to AI resources that would help providers with clinical decisions support and diagnoses in what the Minnesota-based health system calls “event-driven medicine.” It’s designed to help providers in and outside the health system analyze and act on data collected by mHealth devices.
“The dramatically increased use of remote patient telemetry devices coupled with the rapidly accelerating development of AI and machine learning algorithms has the potential to revolutionize diagnostic medicine,With RDMP, clinicians will have access to best-in-class algorithms and care protocols and will be able to serve more patients effectively in remote care settings. The platform will also enable patients to take more control of their health and make better decisions based on insights delivered directly to them.”
As healthcare providers battle an increasing influx of patients and dwindling inventory – including critical personal protective equipment (PPE) supplies like masks, ventilators, and hospital beds – they are relying more heavily on their digital tools and applications than ever before.
Prior to this recent pandemic, research shows that 84 percent of people have experienced problems with digital services in the last year.
In the middle of a global health crisis, there’s no tolerance for bad performance when it’s a matter of a patients’ health.
To improve these experiences, health IT professionals must leverage AI and machine learning to pinpoint the moment digital issues arise and automatically remediate issues.
This saves IT teams time and resources that could be spent creating new services that will further improve the patient and doctor’s experience during the crisis.
Digital strategies that HealthIT leaders must consider to support healthcare professionals regardless of where and when they are providing care.
The big hairy audacious goal of most every data scientist I know in healthcare is what you might call the Integrated Medical Record, or IMR, a dataset that combines detailed genetic data and rich phenotypic information, including both clinical and “real-world” (or, perhaps, “dynamic”) phenotypic data (the sort you might get from wearables).
The gold standard for clinical phenotyping are academic clinical studies (like ALLHAT and the Dallas Heart Study). These studies are typically focused on a disease category (e.g. cardiovascular), and the clinical phenotyping on these subjects – at least around the areas of scientific interest — is generally superb. The studies themselves can be enormous, are often multi-institutional, and typically create a database that’s independent of the hospital’s medical record.
Inevitably, large, prospective studies can take many years to complete. In addition, there’s generally not much real world/dynamic measurement.
The other obvious source for phenotypic data is the electronic medical record (EMR). The logic is simple: every patient has a medical record, and increasingly, especially in hospital systems, this is electronic – i.e. an EMR. EMRs (examples include Epic and Cerner) generally contain lab values, test reports, provider notes, and medication and problem lists. In theory, this should offer a broad, rich, and immediately available source of data for medical discovery.
DIY (enabled by companies such as PatientsLikeMe) represents another approach to phenotyping, and allows patients to share data with other members of the community. The obvious advantages here include the breadth and richness of data associated with what can be an unfiltered patient perspective – to say nothing of the benefit of patient empowerment. An important limitation is that the quality and consistency of the data is obviously highly dependent upon the individuals posting the information.
Pharma clinical trials would seem to represent another useful opportunity for phenotyping, given the focus on specific conditions and the rigorous attention to process and detail characteristic of pharmaceutical studies. However, pharma studies tend to be extremely focused, and companies are typically reluctant to expand protocols to pursue exploratory endpoints if there’s any chance this will diminish recruitment or adversely impact the development of the drug.
Information that may offer medical insights has been locked away in the filing cabinets of doctors' offices.
Researchers at IBM, Berg Pharma, Memorial Sloan Kettering, UC Berkeley and other institutions are exploring how artificial intelligence and big data can be used to develop better treatments for diseases
But one of the biggest challenges for making full use of these computational tools in medicine is that vast amounts of data have been locked away — or never digitized in the first place.
The results of earlier research efforts or the experiences of individual patients are often trapped in the archives of pharmaceutical companies or the paper filing cabinets of doctors’ offices.
Patient privacy issues, competitive interests and the sheer lack of electronic records have prevented information sharing that could potentially reveal broader patterns in what appeared to any single doctor like an isolated incident.
When you can analyze clinical trials, genomic data and electronic medical records for 100,000 patients, “you see patterns that you don’t notice in a couple,” said Michael Keiser, an instructor at the UC San Francisco School of Medicine.
Given that promise, a number of organizations are beginning to pull together medical data sources.
“If you’re dealing with a disease like cancer that can be arrived at by multiple pathways, it makes sense that you’re not going to find that each patient has taken the same path” - John McDonald, a professor in the School of Biology at the Georgia Institute of Technology in Atlanta.
If a driver is traveling to New York City, I-95 might be their route of choice. But they could also take I-78, I-87 or any number of alternate routes. Most cancers begin similarly, with many possible routes to the same disease. A new study found evidence that assessing the route to cancer on a case-by-case basis might make more sense than basing a patient’s cancer treatment on commonly disrupted genes and pathways.
The study found little or no overlap in the most prominent genetic malfunction associated with each individual patient’s disease compared to malfunctions shared among the group of cancer patients as a whole. “This paper argues for the importance of personalized medicine, where we treat each person by looking for the etiology of the disease in patients individually,” said McDonald,
“The findings have ramifications on how we might best optimize cancer treatments as we enter the era of targeted gene therapy.”
The research was published February 11 online in the journal PANCREAS and was funded by the Georgia Tech Foundation and the St. Joseph’s Mercy Foundation.
In the study, researchers collected cancer and normal tissue samples from four patients with pancreatic cancer and also analyzed data from eight other pancreatic cancer patients that had been previously reported in the scientific literature by a separate research group.
McDonald’s team compiled a list of the most aberrantly expressed genes in the cancer tissues isolated from these patients relative to adjacent normal pancreatic tissue.
The study found that collectively 287 genes displayed significant differences in expression in the cancers vs normal tissues. Twenty-two cellular pathways were enriched in cancer samples, with more than half related to the body’s immune response. The researchers ran statistical analyses to determine if the genes most significantly abnormally expressed on an individual patient basis were the same as those identified as most abnormally expressed across the entire group of patients.
The researchers found that the molecular profile of each individual cancer patient was unique in terms of the most significantly disrupted genes and pathways.
“If you’re dealing with a disease like cancer that can be arrived at by multiple pathways, it makes sense that you’re not going to find that each patient has taken the same path” - John McDonald, a professor in the School of Biology at the Georgia Institute of Technology in Atlanta.
Cancer research is something I'm particularly interested in and would try to go into someday and I found this interesting because it shows how medicine has evolved and becoming more personalized.
Researchers at Vanderbilt University Medical Center have used natural language processing technology in an electronic medical records system to identify patients with multiple sclerosis and collect data on traits of their disease course.
The work is significant, researchers say, because much remains unknown about the course of the disease, which varies widely among patients. “Most research studies have focused on the origin of the disease, partly because of the difficulty in ascertaining sufficient longitudinal clinical data to study the disease course,” according to the study published in the Journal of the American Medical Informatics Association.
“Electronic medical records may provide such a tool. We have previously shown that genomic signals of MS risk may be replicated using EMR-derived cohorts. In this paper, we evaluated algorithms to extract detailed clinical information for the disease course of MS.”
The study used algorithms based on ICD-9 codes, text keywords and medications to identify 5,789 patients with MS, and collected detailed data on the clinical course of the patients’ disease to measure progression of disability. “For all clinical traits extracted, precision was at least 87 percent and specificity was greater than 80 percent.”
Researchers uncover new ties between genetics and skin cancer by mining patients’ medical records.
Usually, studying the relationship between DNA and disease involves comparing the genomes of thousands of people with a disorder to the genomes of thousands of people who don’t. These studies can be expensive and may take years, requiring researchers to identify patients, enroll them in the study, and collect the genomic data.
A more cost-effective and speedier alternative is to mine the growing pool of genetic data in electronic medical records as reported by researchers in Nature Biotechnology.
These records chronicle a patient’s health care history, which can include physician’s notes, lab test results, and the billing codes hospitals submit to health insurance companies to receive payments.
The idea behind the new method for genetic discover is to be able to “reuse” the data in these records for medical discoveries, says Joshua Denny, a physician-scientist at Vanderbilt University School of Medicine.
To identify previously unknown relationships between disease and DNA variants, Denny and colleagues grouped around 15,000 billing codes from medical records into 1,600 disease categories. Then, the researchers looked for associations between disease categories and DNA data available in each record.
Their biggest new findings all involved skin diseases (just a coincidence, says Josh Denny, the lead author): non melanoma skin cancer and two forms of skin growths called keratosis, one of which is pre-cancerous. The team was able to validate the connection between these conditions and their associated gene variants in other patient data.
Microsoft Excel is like the swiss army knife for health analytics. It's a familiar and effective tool for surfacing and using almost any type of data from any sources. Learn how health organizations are using Excel as part of their overall BI strategy.
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