Tag Archive for: Healthcare

Shannon Flynn

How Healthcare Is Cracking Down on Data Privacy

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The COVID-19 pandemic emerged more than a year ago, and come March, the United States will also pass the one-year anniversary of the novel coronavirus’ arrival in our nation. Hospitals have become overrun with patients, having to adjust for space even when they’re at full capacity. The colder months are bringing on more infections as well.

With such high demands on health care providers, technology has been an area of assistance through it all. Telehealth in particular allows patients to stay at home and receive care without putting themselves at risk. However, security and privacy concerns accompany this reliance on technology.

The digital world can be dangerous. Hacks and breaches can occur at any time. The novel coronavirus pandemic has accelerated these attacks. Through August 2020 alone, 305 healthcare data breaches occurred — which is up from 2019’s 136 breaches in the same time frame. These vulnerabilities cannot continue to occur, since health care facilities hold vital patient information like Social Security numbers, medical records and financial information.

The industry is resilient, though. Adapting to new norms and protocols is part of the healthcare field. With the new focus on technology to connect patients and providers through the ongoing pandemic, practices have been cracking down on keeping data safe and secure.

Health Care Industry Adapts

Data presents itself in the health care industry in several ways. Standard patient data includes personal information about health history, relationships and private matters. Other forms of data may include connections from medical devices that use the internet — something like a digital blood pressure monitor may transmit data. Then, providers must store and send this data at various times.

The Health Insurance Portability and Accountability Act sets forth two main regulations facilities must follow. The security rule mandates that the use of all electronic personal health data must be stable in any form or use. The privacy rule indicates that all medical records, insurance information and private data must have the best protection.

In 2017, 477 breaches affected about 5.6 million patient records, breaching what should have been secure HIPAA data. To uphold HIPAA regulations and prevent breaches like these from happening, health care providers have taken several steps.

First, education is crucial. Bringing all staff in on up-to-date privacy protocols will go a long way. For instance, using encryption on mobile devices, backing up all data, creating strong passwords and consistently patching and updating the systems and firewalls are critical for staff to understand.

Access is another form of protection. Multi-factor authentication, like passwords, keys, PINs and biometrics, will keep systems secure and only give access to those who need it the most. Then, facilities can monitor data at all times — unauthorized access, emails and transfers. If something suspicious happens, IT departments can see it in real time and flag it or stop it.

Last, consistent evaluations are more necessary than ever. Health care facilities will want to make sure they comply with industry and privacy requirements, and that staff members know the protocols to follow. Then, data privacy remains a top priority.

The Lasting Impact

Vaccines are slowly rolling out and becoming more available to residents across the world. However, even with a vaccine, global spread will slow gradually, especially in areas where cases are high and rising. For instance, the United States cases are still rising and breaking records daily.

Data will continue to be a central focus throughout the pandemic and afterward. Right now, specifically, with big tech companies facing scrutiny and investigations for privacy faults, data is at the forefront of Americans’ minds. Health care companies must excel in ways that big tech has not.

One sign of progress is new mental health startups popping up that focus on virtual dynamics. With services like Real Therapy or Two Chairs, you can make a virtual appointment. Since privacy is already an inherent part of therapy, data privacy will be critical to integrate into these business models.

Getting Ahead of the Curve

While the pandemic may seem uncontrollable at times, health care facilities have more agency. They can smooth relationships with patients and operate more efficiently with stricter data privacy protocols in place. In an uncertain time, ensuring data security is one of the best things health care providers can do.

netapp

Wie künstliche Intelligenz hilft, Krankheiten zu bekämpfen

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Die Herausforderungen im Gesundheitswesen sind gewaltig. Die Kosten steigen, das Geld ist knapp und die Margen sinken. Gleichzeitig fehlt es an Pflegepersonal, die vorhandenen Fachkräfte sind überarbeitet. Kliniken müssen effizient wirtschaften, um Patienten die bestmögliche Versorgung zu gewährleisten – und gleichzeitig Datensicherheits- und Compliance-Anforderungen bei der Verarbeitung der anfallenden Daten sicherstellen.

Der Einsatz von künstlicher Intelligenz (KI) kann dabei helfen, dieses Dilemma zu lösen. Algorithmen werden immer besser in dem, was sie tun – und sie arbeiten exakt, schnell und günstig. KI unterstützt in der Medizin und Forschung dabei, Patienten besser zu versorgen, indem beispielsweise Krankheiten früher erkannt werden können. Mit ihrer Hilfe könnten unter anderem die Gesundheitsausgaben in Europa in den kommenden zehn Jahren um einen dreistelligen Milliardenbetrag gesenkt werden, so das Ergebnis der PwC-Studie „Sherlock in Health – How artificial intelligence may improve quality and efficiency, whilst reducing healthcare costs in Europe“. Des Weiteren haben die meisten Patienten keine Berührungsängste: 54 Prozent wären demnach schon heute bereit, sich auf KI und Robotik in der Medizin einzulassen.

KI, ML und DL als medizinische Unterstützung

Algorithmen können in der Medizin auf unterschiedliche Weisen genutzt werden. KI beschäftigt sich mit Methoden, bei denen Computertechnologien es ermöglichen, menschliches Verhalten zu imitieren. Im Rahmen der medizinischen Bildgebung kann KI beispielsweise schnell Anomalien identifizieren, die für das menschliche Auge zu winzig erscheinen – oder große Datenmengen durchforsten. Ein Computertomograph erzeugt bis zu 640 Schnittbilder bei einem einzigen Scan. Wenn ein Radiologe sie ansehen und bewerten müsste, wäre das mit einem sehr hohen Zeitaufwand verbunden. Eine spezielle KI-Applikation kann die Bilder dagegen schnell analysieren und diejenigen markieren, welche Anomalien aufweisen. Die Radiologen können sich damit auf ihre Hauptaufgaben konzentrieren – Diagnose und Heilung. 

Ein weiteres Anwendungsgebiet von künstlicher Intelligenz innerhalb der Medizin ist der Einsatz von Intelligent Agents (IA), zum Beispiel für die Überwachung von Vitalwerten von Patienten oder als Kontrollmechanismus für die Arbeit des Pflegepersonals, der Ärzte oder Apotheker. Die Anwendungen überprüfen dann automatisch, ob die verschriebenen Medikamente und Therapien zum Krankheitsbild und zu den Werten des Patienten passen. 

Anwendungen aus dem Teilbereich der KI „Maschinelles Lernen (ML)“ lernen eigenständig dazu, je mehr Daten erfasst werden. Chirurgen können ML beispielsweise als Unterstützung verwenden, um den richtigen orthopädischen Eingriff nach einer Sportverletzung vorzubereiten. Die Technologie analysiert Patientendaten und kann die Unterschiede bei Knieverletzungen unterschiedlicher Sportarten sichtbar machen. So stehen dem Arzt detaillierte Informationen zur Verfügung, auf deren Basis er die Behandlung fortsetzt.

Deep Learning (DL) wiederum gilt als Teilbereich von ML und geht noch einen Schritt weiter, indem die Algorithmen nicht nur in der Lage sind, selbständig dazuzulernen, sondern sich auch kontinuierlich zu verbessern. So werden große Datenmengen verarbeitet, aus denen Wissenschaftler mögliche neue Erkenntnisse für Behandlungserfolge gewinnen können. Mit Hilfe von DL lassen sich beispielsweise bisher unbekannte Verbindungen zwischen bestimmten demografischen oder genetischen Indikatoren und unterschiedlichen Krankheiten aufdecken. Ebenso gibt es DL-Algorithmen, die mithilfe großer Datenmengen so trainiert werden, dass sie kleinste Veränderungen in der Zellstruktur erkennen können, die beispielsweise zu Brustkrebs führen. Die Fehlerquote bei der Brustkrebserkennung kann damit um bis zu 85 Prozent sinken, so eine Untersuchung von NVIDIA.

Komplexe KI-Projekte benötigen eine passende IT-Infrastruktur

Damit KI, ML und DL im Gesundheitswesen effektiv eingesetzt werden können, gibt es einige Grundvoraussetzungen. Zunächst einmal sind große Datenbestände notwendig. Diese werden genutzt, um die Algorithmen zu trainieren, damit sie akkurat und autonom arbeiten sowie Muster erkennen und genaue Vorhersagen treffen können. Dabei gilt es so viele Daten wie möglich zu berücksichtigen, unabhängig ob sie über verschiedene Systeme verteilt sind, aus unterschiedlichen Quellen stammen oder von mehreren unterschiedlichen Sensoren gesammelt wurden. Jedoch sollten sie eine hohe Datenqualität aufweisen. Darüber hinaus kann es sich um verschiedene Typen von Daten handeln (strukturiert, semi-strukturiert, unstrukturiert), die sich dynamisch entwickeln und verändern. 

Damit Daten überall dort verfügbar sind, wo sie gebraucht werden, gilt es Datensilos aufzulösen und miteinander zu verknüpfen. KI-Projekte stellen somit hohe Anforderungen an die IT-Infrastruktur hinsichtlich Datenverfügbarkeit und Datenqualität, Skalierbarkeit, Informationssicherheit sowie Genauigkeit mit hohen Ansprüchen an die Performance. Eine solch komplexe KI-Umgebung selbst zu planen, zu implementieren und zu unterhalten stellt viele Unternehmen vor große Herausforderungen. Es gibt jedoch schon umfassende Lösungen am Markt. Beispielsweise bietet NetApp zusammen mit NVIDIA und Cisco eine Lösung für die genannten Herausforderungen: ONTAP AI. Diese Architektur vereinfacht das komplette Datenmanagement: Informationen werden über das System sicher erfasst, weitergeleitet und verarbeitet, zum Training verwendet und analysiert.

ONTAP AI basiert auf einer verifizierten Architektur, die NVIDIA DGX-1 GPU‘s mit NetApp All Flash FAS Storage und Cisco Netzwerken zusammenführt und die Effizienz Ihrer KI-/DL-Umgebungen steigert. Herzstück von ONTAP AI ist die NVIDIA DGX-1, ein vollständig integriertes und sofort einsatzbereites Hardware- und Softwaresystem, das speziell für DL entwickelt wurde. Die DGX Plattform nutzt den Deep-Learning-Software-Stack der NVIDIA GPU Cloud, der für maximale GPU-beschleunigte DL-Performance optimiert wurde. Mit dem schnellen All-Flash-Storage und den weltweit ersten End-to-End NVMe-Technologien sorgen NetApp All Flash FAS Systeme für einen kontinuierlichen Datenfluss. So wird sichergestellt, dass die DGX-GPUs optimal mit Daten zur Verarbeitung versorgt werden und somit ein Flaschenhals hinsichtlich Datenbereitstellung durch die Storage-Systeme vermieden wird. 

Schnelle Diagnose

ONTAP AI kommt beispielsweise bei „BacillAi“ zum Einsatz, einem System zur Behandlung von Tuberkulose der Technologieberatungsfirma Cambridge Consultants. Tuberkulose ist die zweithäufigste Todesursache in Entwicklungsländern, da die Krankheit mit einer aufwendigen Diagnose verbunden ist: Zellen einer Speichelprobe müssen unter dem Mikroskop gezählt werden. Doch dafür gibt es nur wenig ausgebildetes medizinisches Personal. BacillAi vereinfacht diesen Schritt – und liefert zudem genauere und schnellere Ergebnisse. Ein Smartphone nimmt die Bilder der Speichelprobe von einem Standardmikroskop auf. Der DL-Algorithmus identifiziert Tuberkulose-Zellen, zählt sie und bestimmt das Stadium der Krankheit. Die Diagnose erhält der medizinische Mitarbeiter über eine App – somit ist das Ergebnis der Untersuchung zudem digitalisiert.

Fazit 

Künstliche Intelligenz kann das Gesundheitswesen revolutionieren. Unternehmen müssen dafür große Datenmengen aus unterschiedlichen Quellen erfassen, vorbereiten, verschieben, auf sie zugreifen und sie schützen. Damit KI, ML und DL-Projekte erfolgreich sind, brauchen Unternehmen aber eine effiziente Daten-Pipeline und eine Architektur, die eine hohe Performance, Sicherheit und Skalierbarkeit ermöglicht.

Weiter Informationen zum Thema finden Sie HIER.

Lucy Manole

How can AI and Machine learning impact healthcare industry?

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Healthcare industry is a recession-proof one. Even in times of economic meltdown and financial distress, the healthcare industry can hold its own because mankind will always need healthcare. In fact, during the Great Depression in the US, when the economy was facing a severe slowdown, the healthcare industry expanded, adding 852,000 jobs.

Healthcare AI in the US is slated to reach $6.6 billion in value by 2021.

From clinical trials to new drug research & development, and from innovative medical devices to technology like nanoparticles, AI, and ML has touched every point and has the power to transform them completely.

In fact, according to a study by Accenture, AI applications in healthcare can result in global savings to the tune of $150 billion by 2026.

The possibilities are endless, and the results unthinkable if AI can be properly used.

Here are some of the ways AI and ML can impact the healthcare industry:

1. Solving the Iron Triangle

A problem that has plagued the world for many years the triangle aims to solve a fundamental healthcare problem: that of good quality, accessible treatment at low cost.

Providing all three at the same time is a major challenge in healthcare, as the cost of healthcare is usually high. Here, trying to improve one factor harms another.

But AI can solve this problem in the near future without breaking the triangle, by improving the current healthcare cost-structure. The key to it is AI, and smart machines, that the patient can use for self-treatment for the majority of times, cutting down treatment costs drastically, by reducing human contact and improving quality of life.

2. Diagnostics and Imaging

The US FDA has drastically increased investment on AI in radiology and diagnostics. And it’s not without reason.

The IDx-DR became the first AI system cleared by the US FDA to provide diagnostic decisions. It was a breakthrough discovery to detect early mild diabetic retinopathy. The device was accurate 87.5% of the times, and also detected patients who didn’t have the condition, correctly up to 89.5% of times.

The US FDA also permitted marketing of the Viz.AI a type of clinical decision support system designed to analyze CT scan results to identify possibilities of a stroke in the patients and send the results to a specialist to identify any block.

In fact, diagnostics is fast becoming one of the significant drivers of AI investment in healthcare.

These advances can impact the healthcare industry in a novel way. As more and more devices become AI-enabled, the landscape of healthcare delivery will change.

3. Early screening 

Early screening in case of most diseases can drastically improve the mortality rates of patients and cut down treatment costs by over 50%.

Let’s take the example of colorectal cancer.

The 5-year survival for Stage 1 CRC is around 90%, as compared to only 10% for Stage 4.

Early detection of CRC can be ideally treated with a minimally invasive endoscopy at a low cost of less than $5,000 per year. However, in the case of late-stage CRC, it requires multidisciplinary treatment with multiple surgeries, chemotherapy, and radiation, skyrocketing the costs.

And that is why early detection is essential, and that’s exactly what AI can do. There are already apps on the market that are doing this. For example, Autism & Beyond is a revolutionary app that leveraged the power of Apple’s ResearchKit to gather videos of children and detect their preference for the development of autism, using AI software.

AI used for early screening can save billions of taxpayer dollars of taxpayer money every year, and reduce out of pocket expenditure in the US drastically.

4. Drug research & development

According to the California Biomedical Research Association, it takes around 12 years for a drug to be conceived in the laboratory and go to the patient.

Only 1 out of 5000 drugs that are selected for pre-clinical testing are then used for human testing, and only 20% of them make it to the market for human use.

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The cost to develop a new drug now is more than $2.5 billion.

It is only recently that AI is being used in drug research and discovery. The power of AI can be leveraged to streamline the drug discovery and drug repurposing processes. It can identify patients best suited to the trial, can identify patients in the most need for new medications and can predict any side-effects and idiosyncrasies beforehand.

All of these, for a start, can lead to much safer clinical trials with no unwanted drug reactions.

And then, there is the question of lowering costs. In fact, a study by Carnegie Mellon and a German university estimated that AI could lower drug discovery costs by as much as 70%.

This, in turn, will be transferred to patients in the form of lower drug prices, which will increase accessibility to better medications for patients and improve population health in general.

5. Surgery 

AI-enabled robotic-assisted surgeries are taking over the US. They are increasingly being used to reduce surgeon variations and improve quality.

‘Artificial intelligence can help surgeons perform better’ quotes Dr. John Birkmeyer, a chief clinical officer at Sound Physicians.

Advanced analytics and machine learning techniques are being used concomitantly used to unleash critical insights from the billions of data elements associated with robotic-assisted surgery. If used properly, this can help overcome attendant inefficiencies and improve patient health outcomes.

Artificial intelligence helps surgeons make better clinical decisions in real-time during surgery, and helps them understand the dynamics of the patient, especially during complex operations. It also reduces the length of stay of patients by 21%.

This is ultimately reflected in the patient’s post-operative care and long-term health. It also prevents patient readmissions, saving millions of dollars annually.

A study involving 379 orthopedic patients found out that AI-assisted robotic surgery resulted in five times fewer complications as compared to surgeons working alone.

According to Accenture, AI-assisted robotic surgery could save the US healthcare industry $40 billion annually, by 2026.

6. AI-assisted virtual nurses

AI-assisted virtual nurses could well end up saving the US healthcare industry $20 billion annually, by 2026.

They are available 24/7 to answer any patient queries, monitor patients, and guide them in any way they might want.

Currently, they act as a bridge for information exchange between care providers (doctors) and care receivers (patients), to decide what medications to start, the current health status, the most recent test results, and many other things.

It can save the patient many physical appointments with doctors, and also prevent high hospital readmission rates through simple, engaging, and intelligent care.

Care Angel is one of the finest virtual nurses around. Apart from all of the above, it can also provide wellness checks through voice and AI.

Wrap-Up 

AI and ML in healthcare are still at its infancy. Adoption at a large-scale is missing as of yet. To be successful in the healthcare domain, AI and ML need the endorsement of healthcare providers like physicians and nurses.

However, considerable investment is being made in AI in healthcare, and its increasing at a good rate.

AI in healthcare is currently aimed at improving patient outcomes, taking care of the interests of various stakeholders involved, increasing accessibility, and reducing healthcare costs.

In the near future, however, AI and ML, along with technologies like Data Science will take up a much more holistic role to drive healthcare forward.

Thomas Blanchard

The importance of domain knowledge – A healthcare data science perspective

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Data scientists have (and need) many skills. They are frequently either former academic researchers or software engineers, with knowledge and skills in statistics, programming, machine learning, and many other domains of mathematics and computer science. These skills are general and allow data scientists to offer valuable services to almost any field. However, data scientists in some cases find themselves in industries they have relatively little knowledge of.

This is especially true in the healthcare field. In healthcare, there is an enormous amount of important clinical knowledge that might be relevant to a data scientist. It is unreasonable to expect a data scientist to not only have all of the skills typically required of a data scientist, but to also have all of the knowledge a medical professional may have.

Why is domain knowledge necessary?

This lack of domain knowledge, while perfectly understandable, can be a major barrier to healthcare data scientists. For one thing, it’s difficult to come up with project ideas in a domain that you don’t know much about. It can also be difficult to determine the type of data that may be helpful for a project – if you want to build a model to predict a health outcome (for example, whether a patient has or is likely to develop a gastrointestinal bleed), you need to know what types of variables might be related to this outcome so you can make sure to gather the right data.

Knowing the domain is useful not only for figuring out projects and how to approach them, but also for having rules of thumb for sanity checks on the data. Knowing how data is captured (is it hand-entered? Is it from machines that can give false readings for any number of reasons?) can help a data scientist with data cleaning and from going too far down the wrong path. It can also inform what true outliers are and which values might just be due to measurement error.

Often the most challenging part of building a machine learning model is feature engineering. Understanding clinical variables and how they relate to a health outcome is extremely important for this. Is a long history of high blood pressure important for predicting heart problems, or is only very recent history? How long a time horizon is considered ‘long’ or ‘short’ in this context? What other variables might be related to this health outcome? Knowing the domain can help direct the data exploration and greatly speed (and enhance) the feature engineering process.

Once features are generated, knowing what relationships between variables are plausible helps for basic sanity checks. If you’re finding the best predictor of hospitalization is the patient’s eye color, this might indicate an issue with your code. Being able to glance at the outcome of a model and determine if they make sense goes a long way for quality assurance of any analytical work.

Finally, one of the biggest reasons a strong understanding of the data is important is because you have to interpret the results of analyses and modeling work. Knowing what results are important and which are trivial is important for the presentation and communication of results. An analysis that determines there is a strong relationship between age and mortality is probably well-known to clinicians, while weaker but more surprising associations may be of more use. It’s also important to know what results are actionable. An analysis that finds that patients who are elderly are likely to end up hospitalized is less useful for trying to determine the best way to reduce hospitalizations (at least, without further context).

How do you get domain knowledge?

In some industries, such as tech, it’s fairly easy and straightforward to see an end-user’s prospective. By simply viewing a website or piece of software from the user’s point of view, a data scientist can gain a lot of the needed context and background knowledge needed to understand where their data is coming from and how their model output is being used. In the healthcare industry, it’s more difficult. A data scientist can’t easily choose to go through med school or the experience of being treated for a chronic illness. This means there is no easy single answer to where to gain domain knowledge. However, there are many avenues available.

Reading literature and attending presentations can boost one’s domain knowledge. However, it’s often difficult to find resources that are penetrable for someone who is not already a clinician. To gain deep knowledge, one needs to be steeped in the topic. One important avenue to doing this is through the establishment of good relationships with clinicians. Clinicians can be powerful allies that can help point you in the right direction for understanding your data, and simply by chatting with them you can gain important insights. They can also help you visit the clinics or practices to interact with the people that perform the procedures or even watch the procedures being done. At Fresenius Medical Care, where I work, members of my team regularly visit clinics. I have in the last year visited one of our dialysis clinics, a nephrology practice, and a vascular care unit. These experiences have been invaluable to me in developing my knowledge of the treatment of chronic illnesses.

In conclusion, it is crucial for data scientists to acquire basic familiarity in the field they are working in and in being part of collaborative teams that include people who are technically knowledgeable in the field they work in. This said, acquiring even an essential understanding (such as “Medicine 101”) may go a long way for the data scientists in being able to become self-sufficient in essential feature selection and design.

 

Thomas Blanchard

Data Science and Predictive Analytics in Healthcare

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Doing data science in a healthcare company can save lives. Whether it’s by predicting which patients have a tumor on an MRI, are at risk of re-admission, or have misclassified diagnoses in electronic medical records are all examples of how predictive models can lead to better health outcomes and improve the quality of life of patients.  Nevertheless, the healthcare industry presents many unique challenges and opportunities for data scientists.

The impact of data science in healthcare

Healthcare providers have a plethora of important but sensitive data. Medical records include a diverse set of data such as basic demographics, diagnosed illnesses, and a wealth of clinical information such as lab test results. For patients with chronic diseases, there could be a long and detailed history of data available on a number of health indicators due to the frequency of visits to a healthcare provider. Information from medical records can often be combined with outside data as well. For example, a patient’s address can be combined with other publicly available information to determine the number of surgeons that practice near a patient or other relevant information about the type of area that patients reside in.

With this rich data about a patient as well as their surroundings, models can be built and trained to predict many outcomes of interest. One important area of interest is models predicting disease progression, which can be used for disease management and planning. For example, at Fresenius Medical Care (where we primarily care for patients with chronic conditions such as kidney disease), we use a Chronic Kidney Disease progression model that can predict the trajectory of a patient’s condition to help clinicians decide whether and when to proceed to the next stage in their medical care. Predictive models can also notify clinicians about patients who may require interventions to reduce risk of negative outcomes. For instance, we use models to predict which patients are at risk for hospitalization or missing a dialysis treatment. These predictions, along with the key factors driving the prediction, are presented to clinicians who can decide if certain interventions might help reduce the patient’s risk.

Challenges of data science in healthcare

One challenge is that the healthcare industry is far behind other sectors in terms of adopting the latest technology and analytics tools. This does present some challenges, and data scientists should be aware that the data infrastructure and development environment at many healthcare companies will not be at the bleeding edge of the field. However it also means there are a lot of opportunities for improvement, and even small simple models can yield vast improvements over current methods.

Another challenge in the healthcare sector arises from the sensitive nature of medical information. Due to concerns over data privacy, it can often be difficult to obtain access to data that the company has. For this reason, data scientists considering a position at a healthcare company should be aware of whether there is already an established protocol for data professionals to get access to the data. If there isn’t, be aware that simply getting access to the data may be a major effort in itself.

Finally, it is important to keep in mind the end-use of any predictive model. In many cases, there are very different costs to false-negatives and false-positives. A false-negative may be detrimental to a patient’s health, while too many false-positives may lead to many costly and unnecessary treatments (also to the detriment of patients’ health for certain treatments as well as economy overall). Education about the proper use of predictive models and their limitations is essential for end-users. Finally, making sure the output of a predictive model is actionable is important. Predicting that a patient is at high-risk is only useful if the model outputs is interpretable enough to explain what factors are putting that patient at risk. Furthermore, if the model is being used to plan interventions, the factors that can be changed need to be highlighted in some way – telling a clinician that a patient is at risk because of their age is not useful if the point of the prediction is to lower risk through intervention.

The future of data science in the healthcare sector

The future holds a lot of promise for data science in healthcare. Wearable devices that track all kinds of activity and biometric data are becoming more sophisticated and more common. Streaming data coming from either wearables or devices providing treatment (such as dialysis machines) could eventually be used to provide real-time alerts to patients or clinicians about health events outside of the hospital.

Currently, a major issue facing medical providers is that patients’ data tends to exist in silos. There is little integration across electronic medical record systems (both between and within medical providers), which can lead to fragmented care. This can lead to clinicians receiving out of date or incomplete information about a patient, or to duplication of treatments. Through a major data engineering effort, these systems could (and should) be integrated. This would vastly increase the potential of data scientists and data engineers, who could then provide analytics services that took into account the whole patients’ history to provide a level of consistency across care providers. Data workers could use such an integrated record to alert clinicians to duplications of procedures or dangerous prescription drug combinations.

Data scientists have a lot to offer in the healthcare industry. The advances of machine learning and data science can and should be adopted in a space where the health of individuals can be improved. The opportunities for data scientists in this sector are nearly endless, and the potential for good is enormous.