Online Diagnosis

Tired of this scenario? You’re sitting in the doctor’s office at 10:55, and your appointment is at 11. 11:30 passes by, and you’re still waiting. Finally, at 11:45, you’re ushered into the office, only to spend 10 minutes consulting with a doctor to find out all you need is a little rest. Doctors are sick of it too. They’re booked up weeks or months in advance, and hate running behind schedule. A.I. could help take some of this burden off by offering online diagnosis to patients who aren’t sure how serious their need is. Instead of consulting with WebMd and self-diagnosing, A.I. could help cut down on doctor’s visits—safely.

Your.MD is a startup hoping to usher in this new era of diagnosis. Using a chatbot, probability models, and machine learning, the app can help users evaluate their condition, and decide whether or not it’s time to head to a clinic.

Telehealth Advancement

Telehealth is the use of mobile communications to monitor and sometimes diagnose patients. Phone, email, and video chat are all forms of telehealth, and they cut down on patient travel time and medical costs considerably. Some startups are tackling the more mundane (and time-consuming) aspects of telehealth with A.I., creating bots to remind patients to take their medications, schedule follow-up appointments, and more. One startup, Sense.ly, claims that their follow-up bot “Molly” frees up 20% of a doctor’s day! With ongoing personnel shortages in the healthcare industry, these bots could cut down on tasks that don’t require a doctor’s expertise.

Personalized Medical Assitance

For patients who have trouble remembering to follow their doctor’s treatment plan, A.I. can help. Similar to Siri, Cortana, and other virtual assistants built into many devices, health-specific A.I. personal assistants can keep patients on track. Health apps with reminders and advice are available 24 hours a day to patients, and could help them keep track of their health and well-being.

Image and Treatment Analysis

Diagnostics rely heavily on images from MRI, x-ray, and ultrasound procedure. Skilled physicians can spot anomalies, but it isn’t always easy, and some things slip through the cracks. A.I. has been used to find these abnormalities, allowing for enhanced diagnosis and more efficient treatment. With changes being applied to the health care policy, IBM’s Watson has also been used in oncology applications to create customized treatment plans for patients, based on evidence-based practices.

Drug Research

Drug research is a key factor in helping to treat devastating diseases, but it often proceeds at a slow pace—too slow for many afflicted patients. Drug discovery platforms are now using machine-learning algorithms to help cut down the time in the discovery process, potentially saving thousands of lives in the process.

Risk Predictions

Making a diagnosis early on can sometimes mean the difference between life and death. AI can help predict risk based on a patient’s characteristics, coming up with a personalized profile. Using big data and AI analysis, every aspect of a patient’s health and history can be examined quickly, helping to determine risk and arranging a prevention plan. This would cut down significantly on the number of people dying from illnesses like cancer, thanks to the AI’s predictions and early detection.

The Rapid Advancement of AI

Many of the AI developments in healthcare have been focused on empowering patients with tools they can use at home. However, due to the rapid pace of AI development, it shouldn’t be long before more advanced tools are making their way into medical facilities and helping doctors to better serve patients. AI won’t replace the need for nurses and doctors, but it can help everyone stay healthier and keep hospitals running efficiently.

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Despite the challenges of getting usable data, some passionate visionaries are determined to make difference in healthcare with big data. Women’s health in particular could get a boost from big data—here are some ways startups and medical researchers are tackling the issue.

Breast Health

Since breast cancer affects women almost exclusively, it’s one of the diseases under the greatest scrutiny using big data analysis. The Breast Cancer Registry at Baystate Medical Center in Massachusetts is just one example of big data working to make cancer treatments more successful. By collecting data from women of all demographics who have breast cancer, researchers are able to isolate patterns based on different factors like age and ethnicity. By recording the effectiveness of the treatments they provide, doctors can gain insights on how best to treat individual women, and make clinical predictions based on historical data.

The Susan G. Komen Foundation is also interested in the benefits big data could provide in the fight against breast cancer. In October of 2015, the Foundation’s Big Data for Breast Cancer (BD4BC) Initiative convened in a think tank in New York to examine the current capabilities of big data and explore how best to leverage them in clinical settings, with the goal of providing better treatment options and driving research in cancer treatment. To further the conversation about how best to proceed, another meeting will take place, this time on the west coast.

Menstrual Health

Glow, a company that has developed an app to help women track their cycle, is busy mining their database for information on women’s health, while giving users the power to share only the personal information they want to. They are particularly interested in getting more information about women’s fertility, and answering questions about what factors might affect it. So far, the data they have collected shows patterns in when women tend to start their menstrual cycle, and what types of birth control they use most often.

Clue, another app in the menstrual health space, is focused on helping women throughout their lives, and aims to remove the stigma of menstruation. The app is not only used for tracking a woman’s cycle, but using data collected from the user, the app is able to predict when the user is most fertile, helpful for women who want to get pregnant. Ida Tin, the company’s founder, envisions that the app will one day become more personalized, catering to women from their early menstrual cycles through menopause. Users have made some astounding observations through Clue, including a woman who detected an abnormal pregnancy with her data, potentially saving her life.

Cancer Predictions

Early detection is crucial for successful cancer treatment, and ColorGenomics is working to make that process easier using big data. Using genetic testing, they’re looking for patterns in data to help their customers predict whether or not they’re prone to specific types of cancer. Isolating genes has the added benefit of collecting more information about how many people have the genes that predispose them to develop cancer. While the company’s services aren’t currently accessible to everyone (it’s a private genetic testing company and services wouldn’t be covered by insurance, especially those on government programs), it’s a step in the right direction for using data to predict cancer risk. Now that specific genes have been isolated to indicate breast cancer risk, genetic testing and data analysis is a big step forward for women’s health.

Cancer is incredibly difficult to treat in many cases, which is why much of the big data innovations in healthcare focus on cancer research. MD Anderson’s Bioinformatics and Computational Biology department works to analyze mountains of data and gain insights into how cancer might form and behave. This is big data analysis on a molecular level, and researchers believe that it could be the key to developing new cancer therapies.

Getting the Data

So how are these startups getting the data they need for analysis? Simple. They’re just asking for it—and they’re providing value in exchange. Glow has an interface in their app allowing users to input as much (or as little) information as they’re comfortable with, and the data is anonymized for analysis purposes. As the database grows, the company will be able to get better insights on different demographics of women.

Electronic health record systems are becoming much more common in hospitals all over the country, helping doctors manage individual patients’ care more effectively, but that data isn’t readily available to analysts. For now, manual collection is the best choice for startups working with big data in healthcare. Only those willing to offer personal information are providing these companies with the data they need—which doesn’t provide a comprehensive view of women’s health in the United States.

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Climate Corp is a San Francisco tech firm that was founded by two Google employees, who wanted to, “lead the world to revolutionary solutions to historic problems.” So, they hired hundreds of scientists to comb through the fifty terabytes of big data they were gathering from 2.5 million different sources every day. These scientists have been using that information to make algorithms that can analyze individual farms in order to optimize their crop yields.

Digital Farming

Using big data in the farming industry is known as, “digital farming,” and as of now, it has been claimed that 70 percent of farms have implemented some form of digital tools to improve their yields.

The app that Climate Corp created to aid farmers is called “Field View,” and it gathers information from weather reports and in-field sensors, which record real-time data on temperature, weather, soil moisture, nitrogen levels, and much more. When this data is analyzed, the app can tell the farmer the best day to work the fields, how much water to use, what nutrients to add to the soil, what are the best seeds to plant, how much yield they should expect, what weather conditions to watch out for, and much more.

“We’re using data science to de-convolute what’s going on there,” Mike Stern, Climate Corporation CEO says. “Information on the farm is now being collected the same way we collect information on our cell phones or our iPads.”

The company claims that their cloud-based technology can take the average corn harvest from 168 bushels an acre, and help that farmer turn it into a 530 bushels an acre harvest. That means savings for the farmer and a better planet for everyone else.

This is especially important now, as farmers are beginning to deal with the effects of climate change.

Global Warming

The biggest problems facing farmers today come from natural disasters and freak weather conditions that have the potential to completely destroy a farm.

A report from the United Nations Intergovernmental Panel on Climate Change (IPCC) states that, “global warming could reduce agricultural production by as much as 2 percent each decade for the rest of this century.” That means by the year 2100, we could have a 20 percent drop in crop yields, while at the same time, the population would have grown to anywhere between 9 and 10 billion worldwide.

Climate Corp has detailed their global warming goals in their mission statement: “The Climate Corporation’s mission is to help all the world’s people and businesses manage and adapt to climate change. We aim to help farmers protect and increase their profits with uniquely powerful software and risk management products.”

They are using big data to solve the problem on many fronts, including driving down the cost of farming, finding the best crops to plant in global warming affected areas, and increasing crop yields to help feed the world.

Changing landscapes

Global warming not only causes storms, it also changes the ecosystem, and where certain plants are able to grow. A warmer planet makes it easier to grow crops in the Dakotas, but it is shown to wreck havoc on tropical countries.

For example, a 2012 UN study found that between the years 1970 and 2000, the rainy season in Tanzania has been pushed forward by a full month, and there was a decrease in rainfall by 30 percent. Knowing this data, Climate Corp would make suggestions to the farmers in the effected are to plant crops that are more resistant to droughts and heat.

Map from the World Research Institute shows how crop yields are moving north due to climate change

This northern migration of crops can also mean that different plants and animals are migrating to new areas in order to escape rising temperatures. This means that insects will invade areas where they have never been seen before.

Climate Corp offers their customers the ability to take pictures of mysterious bugs, and send them through the app to get advice from the company on the best way to deal with it.

Costs

Right now, farmers are caught in between a rock and a hard place, as there are only a handful of buyers on the market, who are always trying to lower the price for crops they buy, and only a handful of suppliers, who are also looking to raise the price of fertilizers, pesticides, and seeds. Along with global warming, this trend is predicted to cause food prices to double by 2030.

Climate Corp, which has remained mostly independent despite the buyout, aims to solve these costs by using their big data to optimize the use of farm equipment, water, fertilizer, etc. “We can’t afford to be wasteful,” says Rebecca Shaw, a senior scientist at Environmental Defense Fund. “It’s really important that we get better at understanding what the crop needs and when, and apply only that.”

Saving on fertilizer will not only but down on a farmer’s overhead, it will also cut down on the  amount of fertilizer that is dumped into the ocean, where it creates dead zones.

Climate Corp also gathers data from marketplaces around the world. This can be helpful to farmers in developing countries, who are often so isolated that they do not know what they should charge for their produce. So, Climate Corp also uses big data from market prices around the world and informs farmers what is a fair price for their crops.

 Feed the world 

In the end, Climate Corp, a small company dealing in big data, might have provided a technology that will feed the world during the devastating effects of global warming.

Video – Monsanto’s plan to stop world hunger.

If Bayer just bought the big data technology that will help feed the world, it makes the $66 billion check seem like a bargain.

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The new model of patient-centered care and streamlined cross-training for all healthcare professionals in hospital, clinic, or private practice settings helps to ensure greater employee and patient satisfaction.  Not only is there more continuity between various parties—for example, between front desk administrators, nurses, and surgeons—but there is also increased access to patient records online, allowing pharmacies and doctor’s offices better access to more detailed information and patient information.  With the increased level of access to private information comes a heightened need for information security—necessitating the creation of healthcare informatics specialists who are also trained in system analysis and cybersecurity.

However, the increase in information sharing is ultimately a good thing for both patients and medical professionals because it allows for more transparency regarding prescription medications, side effects, and treatment options.  This increased transparency is especially helpful for patients and their families, as it allows them to better understand the treatment options available to them—as opposed to the old model, which largely left patients and support staff in the dark.

Although the old top-down model likely existed with the best intentions in mind, there have been countless cases of misdiagnoses, conflicting prescription medications, and missing pieces of information required to make informed decisions.  Increased data transparency is also good for hospitals and medical providers, since it lessens the chances of legal consequences in the event of serious side effects as a result of misdiagnosis or patient errors.  For example, if a patient refuses to take their medication, a relapse or an ER visit could be more easily traced back to individual patient choices, rather than medical error or malpractice.

As a result, those with MBA degrees are more qualified for positions in health care management than ever before.  The future of healthcare information systems includes cloud computing, laptops, smartphones, and other options for archiving and digitizing information into readily accessible formats.

To be clear, however, the business sector’s traditional, top-down approach is the polar opposite of the new patient-centered model that takes more cues from the modern healthcare sector, which—as with the new student-centered model in education—is beginning to favor a more egalitarian system of decision-making..

According to Peter Segall, “only 40 percent of most health care employees actively participate in workplace decision-making.”  Because of this, the other 60 percent of healthcare staff tends to be less-than-enthusiastic when management decisions are made—and understandably so.  Encouragingly, however, the top-down model is beginning to seem antiquated to many healthcare reformists who are slowly introducing a more democratic, egalitarian approach to decision making, which has the benefit of increased communication among employees.

Segall also documents several adverse outcomes to the top-down model, all of which affect day-to-day operations of a hospital or clinic: effective communication, employee stress, human capital, morale, and patient outcomes.  Instead of absolute managerial control, a more collaborative management approach has been shown to be effective in increasing communal knowledge and reducing organizational bureaucracy—something from which the business world could learn a lot.

In addition to diplomacy skills, the ideal healthcare administrator would have a working knowledge of healthcare-related IT, including cloud-based data storage, database management, systems analysis, data science, and healthcare informatics.  Healthcare administrators should be able to go from a patient’s office to a meeting with the hospital’s board of directors to an executive boardroom.  In that boardroom they might give a presentation using modern data storytelling, social media, cloud-based networking, and data-sharing techniques that effectively illustrate patient information on both an individual level as well as a hospital-wide level.

In other words, healthcare administration consists of many proverbial hats.  It’s exciting to see what those hats will look like, come 2017 and beyond.  Share your experiences with data sharing and healthcare IT in the comments section, below!

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Reducing malpractice is just one of the ways big data is revolutionizing the healthcare industry. Additionally, big data is the backbone of artificial intelligence technology, which will eventually be used to scan cloud-based data to help prevent misdiagnosis and transcription errors. Even now, some hospitals are moving to cloud systems to help reduce errors, and integrate patient records more efficiently.

Prescription errors have become a major problem, and big data is one of the tools used to prevent prescriber mistakes. Modern Healthcare reports that MedAware, a new company founded by Dr. Gidi Stein, uses big data to reduce prescription drug errors by scanning patient data and flagging any prescriptions that don’t match up with the records on file. If an error is detected, the drug will not be ordered until the physician confirms the order or selects the correct drug. Current alert systems can catch drug interactions and unusually high doses, but are unable to determine if the wrong prescription has been chosen. Though MedAware has not yet been implemented into hospital systems, it is currently undergoing testing, and healthcare professionals are extremely optimistic about the potential of the software.

Aside from patient safety, the overall healthcare experience can be improved by big data as well. For example, the Cleveland Clinic was able to improve its patient experience significantly by using analytics to examine patterns and determine what patients really wanted. Once they had the data, they created a plan for improving on those areas, which increased patient satisfaction overall.

Tackling Opioid Addiction with Big Data

Opioid addiction in the U.S. is a major problem that has been intensifying over the last several years. Opioids are very dangerous when misused, and include both strong prescription painkillers and heroin. Reports show that opioid addiction costs Americans $484 billion dollars annually. Big data is helping to fight addiction through analysis of several aspects of the opioid crisis, including demographics, trends, and pharmacy records by area which allows officials to see patterns of certain patients going to several doctors for prescriptions and providing insights on medical professionals who are writing too many opioid prescriptions.

Meritalk notes that in Maynard, MA, an area with unusually high addiction rates compared to the rest of the state, one nurse practitioner accounted for 43.8% of the opioid prescriptions written. Specific information like this helps officials curb addiction by ensuring accountability and helping individuals with worrisome patterns. Many states have seen great success using this approach, with fewer opioids prescribed and fewer patients “doctor shopping” to get access to more prescriptions. Officials hope that more providers will start using electronic prescription systems in the near future to further monitor the activity of opioid distribution.

Integrating Medical Imaging and Big Data

Medical imaging is a vital diagnostic tool and around 600 million imaging procedures are performed each year. Manual analysis and storage of these images are time consuming and expensive, requiring radiologists to examine each image individually and requiring hospitals to store the images for several years. Carestream explains how big data could radically change the way medical images are read by using algorithms to analyze the information from the pixels and identify specific patterns within these images. This analysis would then be converted into a number for the physician’s use to help with diagnosis—potentially preventing disease instead of just diagnosing it. Currently, radiologists are using this technology as an assistive tool, but one day it may be able to stand on its own, resulting in a more efficient and accurate imaging process.

A Revolutionary Tool

On the surface, big data may seem like just another analysis tool, but its depth and efficiency cuts down on the time and resources needed to solve the biggest problems in the healthcare industry. By using big data, our hospitals, clinics and healthcare providers can provide a safer and more efficient patient experience for everyone, improve outcomes and create better communication.

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Since the 1990s, businesses have used data mining for things like credit scoring and fraud detection. Now, a number of healthcare organizations are also beginning to see the potential benefits of data mining and predictive analytics.

      In healthcare, data mining has proven effective in areas such as predictive medicine, customer relationship management, detection of fraud and abuse, management of healthcare and measuring the effectiveness of certain treatments.

The purpose of data mining, whether it’s being used in healthcare or business, is to identify useful and understandable patterns by analyzing large sets of data. These data patterns help predict industry or information trends, and then determine what to do about them.

In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiencies, improve patient quality of life, and perhaps most importantly, save the lives of more patients.

Proven application of data mining

Data mining has been used in many industries to improve customer experience and satisfaction, and increase product safety and usability. In healthcare, data mining has proven effective in areas such as predictive medicine, customer relationship management, detection of fraud and abuse, management of healthcare and measuring the effectiveness of certain treatments.

Here is a short breakdown of two of these applications:

• Measuring Treatment Effectiveness –This application of data mining involves comparing and contrasting symptoms, causes and courses of treatment to find the most effective course of action for a certain illness or condition. For example, patient groups who are treated with different drug regimens can be compared to determine which treatment plans work best and save the most money. Furthermore, the continued use of this application could help standardize a method of treatment for specific diseases, thus making the diagnosis and treatment process quicker and easier.
• Detecting Fraud and Abuse – This involves establishing normal patterns, then identifying unusual patterns of medical claims by clinics, physicians, labs, or others. This application can also be used to identify inappropriate referrals or prescriptions and insurance fraud and fraudulent medical claims. The Texas Medicaid Fraud and Abuse Detection System is a good example of a business using data mining to detect fraud. In 1998, the organization recovered $2.2 million in stolen funds and identified 1,400 suspects for investigation. To recognize its success, the Texas system received a national award for its innovative use of technology.

Data mining and its effect on privacy

Data mining possesses great potential for the healthcare industry, but it also comes with a few privacy concerns. Massive amounts of patient data being shared during the data mining process may leave some patients worried that their personal information could fall into the wrong hands. However, experts argue that this is a risk worth taking.

“There will be criminals. There will be people who are bad actors. At some point, something is going to get out,” Thomas Graf, chief medical officer at Geisinger Health System told The Washington Post in a 2014 article. “It’s not an irrational fear. At the same time, people die driving every year and we still choose to drive cars, or most of us do. It’s a risk every person has to decide where they fall on the line.”

Others have suggested letting patients choose whether their information can be used for data mining purposes and then providing a tax break benefit to encourage patients to get involved.

“The goal in healthcare is not to protect privacy, the goal is to save lives,” David Castro, Director of the Center for Data Innovation told The Washington Post.

The future of data mining

The shift from written to electronic health records has played a huge part in the push to use patient data to improve areas of the healthcare industry. The adoption of electronic health records have allowed healthcare professionals to distribute the knowledge across all sectors of healthcare, which in turn, helps reduce medical errors, provide thorough documentation and improve patient care and satisfaction.

Data mining is also projected to help cut costs. If the U.S. healthcare industry continues to use big data to drive efficiency and quality, the potential value could reach more than $300 billion per year, according to a 2011 report from the McKinsey Global Institute.

The future of healthcare may well depend on using data mining to decrease healthcare costs, identify treatment plans and best practices, measure effectiveness, detect fraudulent insurance and medical claims, and ultimately, improve the standard of patient care.

Originally appeared on USF health

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Dr. Minor, Dean of Stanford Medicine, surgeon and scientist spoke of how precision medicine should move towards becoming more preventive, in a Forbes article. He says today instead of curing the disease when it strikes, the focus should be on preventing the disease before it strikes. And this can only be enabled when data meets hands-on healthcare.

The amount of healthcare data has exploded over the past few years giving physicians and researchers more opportunities to evaluate data for assessments. Data is now being used heavily in clinical trials to assess which treatment will work for a larger group of people. Doctors now need to assess large data pools of information from ‘the lens of an individual patient’. For that, doctors now need to apply a working knowledge of data science to medical science and achieve better patient outcomes.

At the same time, more people today actively manage their own health with the use of wearable devices and online medical websites. What this demographic mainly demands is a user-friendly healthcare experience, that allows them to manage their health easily. Healthcare providers and medical schools too are increasing collaboration with computer science, engineering and healthcare business innovators with a view to adopting new technological developments into medical training and practice. Students and doctors are now leveraging data to monitor pandemic strains, decipher the causes of autism, detailing the human genome system etc. Schools like Stanford are collaborating with giants like Google to understand how to draw the picture of a healthy human being by collecting anonymous genetic and molecular information.

However, while doctors leverage data to gain very significant insights into patient’s health and behavior, the time-honored practice of listening to the patients cannot be discounted at all. The data that you get from the patients directly is more nuanced and reflects how the symptoms of the disease manifest and what they fear. This information in combination with data generated from other sources can help doctors develop a more holistic approach to healthcare.

Originally appeared on LinkedIn.

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Healthcare providers are using two types of data, namely, retrospective data, that collects event-based information, and real-time clinical data. However, despite its widespread use, there is still a lot that this sector needs to do to leverage the complete advantages of big data. Poor usage practices that have led to lost or stolen data have somewhat marred the reputation of data and made patients wary of sharing their personal information. If the issue of data security can be addressed then big data can help healthcare gain more opportunities to provide better treatments, at better costs by proving access to specific symptoms and elements of diseases that were previously not possible. Big data can also provide better patient insights to physicians and help them identify signs of impending bad health. This can aid preventive medication as the physicians can, with these insights, flag and treat a disease before it escalates.

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Not only does healthcare informatics allow for electronic health records to be streamlined and available across departments and between hospitals, clinics, and healthcare providers, but information about billing, insurance, and healthcare expenses is also connected and electronically available. Moreover, patients are educated via various mediums: mobile applications, email, and conventional mail, as well.  Because each patient’s data is stored in one place, it becomes easier to compile the results of research and analysis of related healthcare costs, trends, or threats in a way that facilitates utilization of valuable information.

For both patients and healthcare providers, health informatics help streamline all relevant data and research into one place.  Electronic health records provide greater access to documentation, increasing the connection between patients and doctors, as well as enhanced decision support and the most compatible prescriptions.  Informatics also help educate patients about their current health profiles: for example, young Type 1 diabetes patients reported that an electronic log of their glucose levels and insulin use is more fun and preferable to use in the future than a paper log.  Electronic systems not only allow for direct access to health records; they also keep patients informed and involved.

The way of the future in healthcare is shifting, due to current advancements in technology becoming increasingly integrated into consumer lifestyles.  The large amount of data being recorded and collected demands integration into healthcare information systems, with its integrity verified and its content securely stored.  Four technologies shaping the future of healthcare information systems include transaction and reporting systems, wearable technology, integrated data streams, and automated alerts.  The integration of data with patient-related activities may help prevent a number of health-related problems.

For example, let’s say a patient has three different doctor’s appointments in one week and decides to wait until the end of the week to have her local pharmacy fill all the prescriptions in one visit.  Ideally, all three doctor’s offices share information electronically with the pharmacy so that the patient’s information—including possible allergies and potentially problematic drug interaction—are all visible and prominent in the pharmacy’s computer information system.  The pharmacist may notice that two of the prescriptions scheduled to be filled don’t go well together.  They can then consult the doctor, who can suggest alternative prescriptions.

There are a number of different types of wearable technology out there, already.  Two examples include heart monitors and Fitbits. The information collected by a Fitbit, for example, is extremely valuable, but it’s also potentially revealing and personal.  While information about sleep patterns is fairly innocuous, data about frequency or location of intercourse may be something you’d prefer to keep to yourself.  Uploading information to a cloud-based location further complicates matters.  However, data that charts the progression of patient health and behavior over time would allow for more accurate diagnoses and may eventually enable doctors to visit with patients remotely.

*   *   *

As the field of informatics advances over the next few years, we will likely see greater cohesion between information systems and tracking ability that will further develop patient’s’ ability to consult with medical professionals remotely.  Have you had any experiences in utilizing health informatics?  Share your thoughts in the comments below!

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Big Data Analytics Helping With Patient Care

Although doctors can be a source of vast medical knowledge, the modern medical landscape is slowly shifting emphasis away from doctors opinions to the hard facts of Big Data analytics. 2016 will be no exception to this progressive, technology-driven, evolutionary trend in the healthcare industry. While a team of doctors may be able to assess and speculate about a patient’s condition, the simple fact remains that computers are already starting to produce these results faster. What a doctor is unable to do for their patient is to rapidly scour thousands, if not millions, of similar case studies in the matter of seconds. As more cloud-based Big Data medical repositories weigh in on patient care, much of the hard thinking may be removed from the doctor’s daily routine. Even though doctors may feel that these enhanced computer systems have encroached on their profession, according to this source, many analysts would like to see more Big Data doctors enter the field as we step forward into the very near future. One of the major reasons for this is because doctors collect tons of important medical data, but they are not necessarily very efficient at knowing what to do with that data to improve patient care, lower costs and improve ROI.

Big Data Versus Big Disease

According to BCBS, Blue Cross and Blue Shield are on a major push to make a huge dent in heart-related deaths. BCBS is taking steps to employ the use of Big Data to help prevent 1-million heart attacks by 2017. By sharing the information they have gathered on heart health with other medical institutions, BCBS hopes to demonstrate how Big Data can be used to arrest Big Disease in ways not yet experienced in the medical industry. Since Big Data analytics make it possible to discover correlations in aggregated data, which would normally go overlooked by doctors, 2016 could end up being a major turning point in shifting the healthcare system towards a more rigorous disease prevention model for healthcare. If successful, such efforts would significantly serve to help lower insurance costs across the board for a condition that proves to be one of the leading causes of death in the world.

Bringing Medical Practices up to Date

While many doctors have resisted moving forward with technology, the need for Big Data analytics and cloud services, which are intended for such purposes as: EMR record-keeping and improving a medical practice’s business efficiency, makes the need for an upgrade in office technology a must for lagging doctors in 2016. To function without the help of quality, medical-management software will only serve to distance tech deficient doctors from their patients and the ongoing forward progression of the medical industry itself. As with any changing industry, doctors need to recognize that their patients are emerging digital natives who expect their physicians to be tech savvy and ready to connect in the coming year. For many medical professionals, this ability to connect digitally with their patients will play hard into how well their business operates in light of a dying elderly, less tech experienced customer-base.

Conclusion

Technology has long been a point of leverage for increasing the usefulness and efficiency of modern healthcare. As technology reaches deeper into the daily operations of medical practices, doctors will be able to stay on top of the latest information Big Data and cloud services will be offering these medical professionals. With better analytics at the average doctor’s fingertips, patient diagnostics and preventative measures will continue to improve throughout the healthcare landscape. For patients, this means better prevention and treatment all around. With the connectivity that cloud services provides to medical professionals, this also means moving towards improvements that will keep doctors closer to the cutting edge of what their industry has to offer.

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BYOD Definition and Benefits

In simple terms, BYOD is a concept whereby employees and professionals are allowed to use their mobile devices for work related aspects. It is also being encouraged in Healthcare facilities given the number benefits that associated with the trend. If we talk about its advantages, the following are some of its advantages:

1.  Utilization of Healthcare Infrastructure

The main advantage with BYOD is that the professionals can eliminate the need to carry multiple devices to achieve tasks. As a result, it leads to better utilization of infrastructure deployed by health care facilities. A good example of such an infrastructure is a wireless communication network in form of WLAN. Through such a network, cloud computing is feasible and very efficient in allowing user efficiency. Most health care companies spend a great deal of time and money in setting up such infrastructure so why not make good use of them?

2. Employee Satisfaction and Attraction of Talent

The key to business development is having a good Human Resource management base to build a company on. Human Resource entails proper employee management coupled with spot on talent recruitment techniques.  A research-by Xigo’s 2012 “Mobility Temperature Check” study found that the main reason behind the deployment of BYOD in organizations is to keep employees happy. Think of it this way, employees use devices like Smartphone in virtually any activity, thus wouldn’t it be wise to afford them the same Smartphone to access various functions at work? This next generation is full of passion for mobile devices. It is even safe to say that our lives are driven by this kind of technology. In terms of attracting talent BYOD adoption in health care could be used to lure new employees owing to the flexibility it affords. The point here is that BYOD can be used as selling point in a bid to beat off competitors in hiring raw talent.

3. Employee Productivity

BYOD is known be provide the flexibility that brings the best out of employees. Employees tend to visualize the chance to work from any place without experiencing too many impediments. In fact, research has shown that a health care organization’s productivity peaks when an employee is allowed to access health care functions from home. An employee can log into work related applications at home working extra hours, thus boosting organizational productivity unlike in the traditional way of working.

4. Reduced Costs

BYOD is a great trend to reduce the cost for health care organizations around the world. The key reason is that an organization can shift a portion of its hardware acquisition costs to employees in a passive manner. The tricky part with technological hardware and software is the rate at which it turns obsolete, thus companies are always forced to incur costs frequently to get new ones. With BYOD, employees use the latest technology that can solve the problem of spending budget for new models.

BYOD Risks

Of course with any technology, there are some cons associated. With BYOD, there are a number of challenges that are moving back the trend in health care, but the major one has got to be a data security breach. Statistics even show that the number of health care related data breaches has grown more popular than any other security breach! In 2013 for example, it accounted for 44% of all breaches, according to the Identity Theft Resource Center. Perhaps what has been driving these numbers is the invaluable nature of Personal Health Information in identity theft.

Another fact is that 1 out 10 American citizens have been affected in one way or another by a health care data breach. The interesting fact is that employee negligence was found to be the loophole behind this shocking statistic. According to the HIMSS Security Survey, employee access and handling of data is a major concern in the fight against this vice. Now, this is where the data security risk with BYOD make health care organizations cautious. The question that begs for an answer then is how can BYOD data be secured?

Five Strategies to secure healthcare data in BYOD

Regardless of a companies’ stand in terms of BYOD, the trend has in a way or another passed into many institutions. This is to imply that BYOD data security is colossal to companies that have policies against the BYOD trend and employees that are already implementing it. There are a few strategies that can be effectively used to curb data breaches in BYOD:

1. Risk Assessment

The first step before implementing BYOD in health care is to assess all the risks associated with the system. For example, in the healthcare business, how many staff members have access to the patient information? The essence of this is to identify any loopholes that you will think possible in the future. Carrying out a data inventory and threat analysis plus an analysis of the current BYOD status is one way to ensure that the Personal Health Information will be kept safe on implementation.

2. Mobile Device Management

BYOD security in communication is best achieved through protecting data as it flows from one end to another. Mobile Device Management (MDM) is one powerful way of implementing such kind of security in mobile communications. This approach utilizes software platforms in aspects like configurations, software updates while also keeping an eye on the security of information. Choosing an MDM system that can manage phones from different platforms is a cost effective option for health care institutions.

The other key part of MDM is encryption of data in networks and devices. This can be done through the use of multi factor authentication and encryption algorithms. Containerization is another scheme that works effectively. In containerization, it allows IT to not only secure the data on a device, but also grants control to apps to access data and manage data sharing.

3. Policies

Create clearly defined rules and regulations before implementing BYOD is also one very important element in fighting data breaches in health care.  This is to ensure that users have a clear understanding of the device specs allowed, their roles and consequences of breaking regulations. For example, users should not be allowed to share PHI (personal health information) through file sharing platforms.

4.  Invest of Securing PHI

The biggest mistake that many health care organizations make is that they focus on securing devices in BYOD environment instead of PHI.  There is a limit at which one can secure a mobile device, in this way, one is advised to try and secure data flow and data access before allowing users to start using the system. Drawing a map of data flow is normally used to check and deploy PHI security procedures.

5. Do Not Compromise on Usability

Data security, integrity will not be enforced well without great user experiences in BYOD. User mobility is the ultimate goal of BYOD and it should not be compromised even though security layers have to go together with this feature. The best way to do this is to make sure of enough support in terms of IT staff that will smooth the use of BYOD systems in the health care facility

Conclusion:

It is expected that health care will grow prone to hackers in the future, thus data security will grow primarily in all health care facilities. As hacking grows in size so should data security experts. To be safe, protection is a great step towards mitigating cybercrime disasters-prevention will always be much better than cure!

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A majority of drug discovery projects now use bioinformatics tools to dig out appropriate molecule structures for drugs and test their interactions with host molecules in humans. Vast publicly available databases exist which need to be mined in order to extract meaningful information. The idea is to establish in-silico whether the new drug molecule will work in-vivo or not. The advent of new drugs has been stagnant over the last few decades, which is expected to overturn owing to the availability of vast computational resources and the growing interest of scientists to explore computational solutions for such problems.

As with existing molecules, pharma faces problems in coming up with suitable dosage forms (tablets, capsules, injections, sprays, ointments, etc.) and formulations (mixing the active ingredient with some other chemicals called excipients that are mostly inert stabilizing entities). Suitable dosage forms are chosen in view of the target user, drug delivery requirements, or based on experience. For the purpose of this article, let’s take tablets as they comprise of more than 80% of pharma products. It is imperative to get the formulation right every time. The process involved in creating a single tablet requires the right amount of each powder mixed to the right consistency and then compressed into a tablet of a specific strength. Now consider that a single tableting machine is capable of churning out a hundred thousand tablets in an hour. How much variation can that cause? A lot. That is exactly why regulatory authorities in different countries are so strict about the quality of dosage forms. In efforts to meet standards, the pharma manufacturer cannot do without manual optimizations, experienced personnel, and inevitably, monetary losses.

Improvement of these processes would mean safer drugs, possible reduced prices, and shorter time to market. Scientists like Bourquin, Rowe, and Mendyk have been working to create data-driven solutions to exactly such problems. Small amounts of data, precisely based on lab experiments imitating the manufacturing line are used to create intelligent computational models which are representative of a particular process for powders with certain properties. The models are mostly based on detailed formulation characteristics and manufacturing process conditions. Different versions of random forests, fuzzy systems, and neural networks are being explored to find solutions to different problems. Such models help detect failures beforehand, provide optimized solutions, and help reduce variation among different batches. Furthermore, there is a case for transparency in models since the regulatory authorities require full disclosure of methods used. Black box methods are not well suited for such problems; hence, the use of symbolic regression looks promising. The use of R as a language of choice is more prevalent because the FDA endorses it for statistical analyzes. Systematic studies are being conducted in a generously funded EU project called IPROCOM.

Quality characteristics of tablets are important because they are an indirect indicator of efficacy and bio-availability of drug once swallowed. Finding out how much drug actually went into the blood stream is yet another cumbersome process, not to mention the enormous risks involved. A standard way is to conduct clinical trials and collect that data. It goes without saying that conducting clinical trials is a risky and expensive exercise – and it would be great to use tools which can circumvent the process where possible. Mendyk has developed an empirical way of establishing the clinical trial in silico using data from lab experiments only (open source tool(s) available on Sourceforge).

Data methods are a great tool of empowerment to the research and development teams of pharma companies – big and small alike, to develop new drugs and to make existing ones cheaper and safer. Data scientists should focus their energies towards this field – I think wonders are waiting to happen.

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