Table of Contents
The healthcare industry is one of the most demanding in terms of deploying innovative technologies due to the need for efficient tools for data collection and processing. Data science provides the healthcare industry with several valuable solutions that not only facilitate data management but offer a fresh take on it. Let’s have a look at the most popular use cases of data science in healthcare and the ways it reshapes the industry.
Data scientists in healthcare: a quick recap of the role
As we discussed in one of our previous articles, a data scientist is a person responsible for studying the data and extracting valuable insights from it. So where does a data scientist fit in healthcare?
The healthcare industry processes massive amounts of data on a daily basis. This data comes from wearables, electronic health records, and medical images, is exchanged between the departments and is stored in both electronic and physical formats. Therefore, one needs a sophisticated and powerful tool not only to process but to analyze it as well. This is where data scientists step in and use their skills and knowledge to design ML-based models for efficient data analysis. These models are capable of thorough and deep analysis and prove to deliver high-quality and accurate results. An example would be the detection of certain conditions by screening a medical image or predictive analytics for disease prevention.
The benefits of data science in healthcare
Taking into consideration the fact that the smallest piece of information in healthcare can literally save a life, it’s obvious that the importance of proper data processing and analysis is incredibly high. Let’s list the biggest benefits of using data science in healthcare:
- Faster and more accurate data processing: this includes processing of EHRs (electronic health records) and medical images;
- Identification of warning indicators (i.e. disease symptoms) among patients;
- Assistance in diagnostics;
- Reduced risk of wrong treatment and more accurate treatment selection;
- Improved patient care.
The use cases of data science in healthcare
While the general purpose of applying data science to healthcare is better and faster data processing, there are several specific use cases to know about.
Genomics is the study of an entire organism’s genes and DNA and their sequencing. Thanks to genomics, medical scientists learn more about how a disease impacts one’s organism or how this organism responds to a specific drug. Needless to say, genomics is a vital field of study but it’s also very challenging due to millions of cells in a human body that need to be studied and analyzed.
Data science made a significant contribution in studying genes and DNA. Due to its capability of almost instant processing of terabytes of data and the independent research of patterns and dependencies, machine learning technology is now one of the primary tools to use in genomics. It allows medical scientists to study DNA sequencing and genes much faster, minimizes the number of possible errors, and helps accurately determine the relationships and dependencies between genes and drugs or diseases. This, in turn, has a long-term impact on creating drugs for hardly curable or non-curable diseases.
This point is related to the one discussed above. Drug research has always been a cumbersome and long-term process as it involves a number of critical steps, like identifying the best components and their combinations and then examining how they will react in real life.
Thanks to machine learning and predictive analytics, medical scientists can now significantly speed up the drug research process. It has now become easier not only to analyze the components and their compatibility but also to forecast the possible reactions of an organism and the possible effects of a drug. Hence, data science in healthcare can cut off several years of manual drug research and it can significantly reshape the way scientists work on drugs and their effects.
Medical images such as X-ray images are the primary source of important information about the patient’s health. However, it’s quite challenging to identify minor deviations manually, without specialized tools. But if you apply computer vision, things instantly get better.
Computer vision software is capable of deep image analysis and performs much better than people in terms of accuracy of screening. Here are some useful functions of computer vision:
- Detection of deviations and anomalies related to diseases;
- Image denoising and processing;
- Image interpretation and recognition.
As you see, computer vision (aka the subset of data science) adds more clarity and accuracy to the process of image processing and serves as a valuable asset.
Another great use case of data science in healthcare is predictive analytics. Predictive analytics plays an important role in the development of the healthcare industry. Due to the capability of ML-based models to draw forecasts and predict the behavior of one’s organism or its reaction to a drug, medical scientists can better understand the development of diseases, the possible reaction to a drug, possible health risks, and much more. Here is how predictive analytics contributes to healthcare:
- Predicts one’s condition and possible development of disease;
- Helps calculate the possibility of disease to occur;
- Predicts the behaviour of a certain drug and its impact on one’s organism;
- Helps better manage pharmaceutical logistics.
Patients’ health monitoring
Remember we mentioned wearables as one of the primary data sources? Such devices as smartwatches, for example, can produce a lot of useful data: heartbeat, blood pressure, body temperature, etc. All this data can be collected, added to one’s health record, and used for diagnostics and treatment.
Data science contributes to better patients’ monitoring by providing tools, necessary for accurate management of the collected data. One of the most common tools is the IoT technology that is responsible for the data collection and AI for data processing and automation of certain tasks.
Smart virtual assistants
One more use case for data science in healthcare is the development of smart, AI-powered virtual assistants (similar to Siri or Alexa). A good example would be a mobile application that a patient can interact with. The basic functions of such an app normally include:
- Regular reminders about the medicine intake;
- Contacting doctors in case of an emergency (after analyzing the patient’s state);
- Monitoring the patient’s state;
- Voice and image recognition for better health monitoring;
- Psychological assistance (to a certain extent).
Note that the use of such apps can also contribute to the reduction of admission and readmission costs as the patients can monitor their health from home and contact the hospital only in case of acute emergency. While a virtual assistant cannot fully replace a medical professional, it can at least provide minimal care and guidance so a patient can always be informed on the state of their condition.
Data science in healthcare plays an important role as it fully unveils the data and introduces new approaches to its analysis and use. While there is still a long way to go, the healthcare industry is already seeing a major transformation in terms of its processes and technologies used. We can only guess how the industry will transform in ten (or even five) years but for now, data science seems to be its trump card.
And there are already numerous software solutions in the market that allow introducing data science to your organization in a smooth and efficient way. There is also an option of custom development in case your organization has particular needs and specific requirements – contact us to learn more about each option and the possible solutions that you can get.
Sekta ya huduma za afya ni moja ya mahitaji zaidi katika suala la kupeleka teknolojia za ubunifu kutokana na haja ya zana ufanisi kwa ajili ya ukusanyaji wa data na usindikaji. Sayansi ya data hutoa sekta ya huduma za afya na ufumbuzi wa thamani ambao sio tu kuwezesha usimamizi wa data lakini pia kutoa mtazamo mpya juu yake. Hebu tuangalie kesi maarufu zaidi za matumizi ya sayansi ya data katika huduma za afya na njia ambazo zinaunda upya tasnia. Data Scientist katika Huduma za Afya: muhtasari wa haraka wa jukumu Kama tulivyojadili katika moja ya makala zetu za awali, mwanasayansi wa data ni mtu anayehusika na kusoma data na kutoa ufahamu muhimu kutoka kwake. Kwa hivyo, ni wapi mwanasayansi wa data anafaa katika huduma za afya? Sekta ya huduma za afya inashughulikia kiasi kikubwa cha data kila siku. Takwimu hizi zinatoka kwa vifaa vya kuvaa, rekodi za afya za elektroniki, na picha za matibabu, zinabadilishwa kati ya idara na huhifadhiwa katika muundo wa elektroniki na wa kimwili. Kwa hiyo, mtu huhitaji kifaa cha hali ya juu na chenye nguvu si tu cha kuchanganua bali pia cha kuchanganua. Hapa ni ambapo wanasayansi wa data huingia na kutumia ujuzi wao na maarifa kubuni mifano ya msingi wa ML kwa uchambuzi mzuri wa data. Mifano hizi ni uwezo wa uchambuzi kamili na kina na kuthibitisha kutoa ubora wa juu na matokeo sahihi. Mfano ungekuwa kugundua hali fulani kwa kuchunguza picha ya matibabu au uchambuzi wa utabiri kwa ajili ya kuzuia magonjwa. Kwa kuzingatia ukweli kwamba kipande kidogo cha habari katika huduma ya afya inaweza kweli kuokoa maisha, ni dhahiri kwamba umuhimu wa usindikaji sahihi wa data na uchambuzi ni ya juu sana. Hebu orodha faida kubwa ya kutumia sayansi ya data katika huduma za afya: - Haraka na sahihi zaidi ya usindikaji wa data: hii ni pamoja na usindikaji wa EHRs (rekodi za afya za elektroniki) na picha za matibabu; - Utambulisho wa viashiria vya onyo (yaani. - Usaidizi katika utambuzi; - Kupunguza hatari ya matibabu yasiyofaa na uteuzi sahihi zaidi wa matibabu; - Kuboresha huduma ya mgonjwa. Matumizi ya Sayansi ya Takwimu katika Huduma za Afya Ingawa lengo la jumla la kutumia sayansi ya data katika huduma za afya ni usindikaji bora na wa haraka wa data, kuna kesi kadhaa za matumizi maalum za kujua. Genomics ni utafiti wa jeni na DNA ya kiumbe nzima na mpangilio wao. Shukrani kwa genomics, wanasayansi wa matibabu kujifunza zaidi kuhusu jinsi ugonjwa athari moja ya kiumbe au jinsi kiumbe hii inajibu dawa maalum. Bila shaka, genomics ni uwanja muhimu wa utafiti lakini pia ni changamoto sana kutokana na mamilioni ya seli katika mwili wa binadamu ambayo yanahitaji kuchunguzwa na kuchambuliwa. Sayansi ya data ilitoa mchango mkubwa katika kujifunza jeni na DNA. Kwa sababu ya uwezo wake wa usindikaji wa karibu wa papo hapo wa terabytes ya data na utafiti wa kujitegemea wa mifumo na utegemezi, teknolojia ya kujifunza mashine sasa ni moja ya zana za msingi za kutumia katika genomics. Hii inaruhusu wanasayansi wa matibabu kuchunguza mfuatano wa DNA na jeni kwa kasi zaidi, kupunguza idadi ya makosa yanayowezekana, na kusaidia kuamua kwa usahihi uhusiano na utegemezi kati ya jeni na dawa au magonjwa. Hii, kwa upande wake, ina athari ya muda mrefu kwenye kuunda dawa kwa magonjwa ambayo hayawezi kutibiwa au yasiyoweza kutibiwa. Jambo hilo linahusiana na lile lililozungumziwa hapo juu. Utafiti wa dawa za kulevya daima imekuwa mchakato mgumu na wa muda mrefu kwani unahusisha hatua kadhaa muhimu, kama vile kutambua vipengele bora na mchanganyiko wao na kisha kuchunguza jinsi watakavyoitikia katika maisha halisi. Shukrani kwa kujifunza mashine na uchambuzi wa utabiri, wanasayansi wa matibabu sasa wanaweza kuharakisha sana mchakato wa utafiti wa dawa. Sasa imekuwa rahisi sio tu kuchambua vipengele na utangamano wao, lakini pia kutabiri athari zinazowezekana za kiumbe na athari zinazowezekana za dawa. Kwa hivyo, sayansi ya data katika huduma za afya inaweza kukata miaka kadhaa ya utafiti wa dawa za mikono na inaweza kubadilisha kwa kiasi kikubwa jinsi wanasayansi wanavyofanya kazi kwenye dawa na athari zao. Picha za matibabu kama vile picha za X-ray ni chanzo kikuu cha habari muhimu kuhusu afya ya mgonjwa. Hata hivyo, ni vigumu sana kutambua kupotoka ndogo manually, bila zana maalumu. Lakini ukitumia maono ya kompyuta, mambo huboresha mara moja. Programu ya maono ya kompyuta ina uwezo wa uchambuzi wa kina wa picha na hufanya vizuri zaidi kuliko watu katika suala la usahihi wa uchunguzi. Hapa ni baadhi ya kazi muhimu ya maono ya kompyuta: - Ugunduzi wa kupotoka na anomalies kuhusiana na magonjwa; - Picha denoising na usindikaji; - Picha tafsiri na utambuzi. Kama unavyoona, maono ya kompyuta (aka subset ya sayansi ya data) huongeza uwazi zaidi na usahihi kwa mchakato wa usindikaji wa picha na hutumika kama mali ya thamani. Kisa kingine kikubwa cha matumizi ya sayansi ya data katika huduma za afya ni uchambuzi wa utabiri. Uchambuzi wa utabiri una jukumu muhimu katika maendeleo ya sekta ya huduma za afya. Kwa sababu ya uwezo wa mifano ya ML ya kutabiri na kutabiri tabia ya mwili wa mtu au athari zake kwa dawa, wanasayansi wa matibabu wanaweza kuelewa vizuri maendeleo ya magonjwa, athari inayowezekana kwa dawa, hatari zinazowezekana za afya, na mengi zaidi. Hapa ni jinsi utabiri analytics inachangia huduma za afya: - Inatabiri hali ya mtu na uwezekano wa maendeleo ya ugonjwa; - Inasaidia kuhesabu uwezekano wa ugonjwa kutokea; - Inatabiri tabia ya dawa fulani na athari yake juu ya mwili wa mtu; - Inasaidia kusimamia vizuri vifaa vya dawa. Ufuatiliaji wa afya ya wagonjwa Kumbuka tulitaja vifaa vya kuvaa kama moja ya vyanzo vya msingi vya data? Vifaa kama vile smartwatches, kwa mfano, vinaweza kuzalisha data nyingi muhimu: mapigo ya moyo, shinikizo la damu, joto la mwili, nk. Takwimu hizi zote zinaweza kukusanywa, kuongezwa kwa rekodi ya afya ya mtu, na kutumika kwa utambuzi na matibabu. Sayansi ya data inachangia ufuatiliaji bora wa wagonjwa kwa kutoa zana, muhimu kwa usimamizi sahihi wa data zilizokusanywa. Vifaa vya kawaida ni teknolojia ya IoT, ambayo inahusika na ukusanyaji wa data, na AI kwa usindikaji wa data na automatisering ya kazi fulani. Msaidizi wa Virtual Smart: Msaada wa Virtual Smart ni mfano wa teknolojia ya AI inayoweza kutumika kwa Siri na Alexa. Mfano mzuri ungekuwa programu ya simu ambayo mgonjwa anaweza kuingiliana nayo. Kazi za msingi za programu kama hiyo kwa kawaida ni pamoja na: - Kumbusha mara kwa mara kuhusu matumizi ya dawa; - Kuwasiliana na madaktari katika kesi ya dharura (baada ya kuchambua hali ya mgonjwa); - Ufuatiliaji wa hali ya mgonjwa; - Utambuzi wa sauti na picha kwa ajili ya ufuatiliaji bora wa afya; - Msaada wa kisaikolojia (kwa kiwango fulani). Kwa mujibu wa taarifa hiyo, matumizi ya programu hizo yanaweza pia kusaidia kupunguza gharama za uandikishaji na uandikishaji tena, kwani wagonjwa wanaweza kufuatilia afya yao kutoka nyumbani na kuwasiliana na hospitali tu katika kesi ya dharura kali. Ingawa msaidizi wa virtual hawezi kuchukua nafasi ya mtaalamu wa matibabu, anaweza angalau kutoa huduma ndogo na mwongozo ili mgonjwa aweze kuarifiwa kila wakati juu ya hali ya hali yake. Sayansi ya data katika huduma za afya ina jukumu muhimu kwani inafunua data kikamilifu na kuanzisha mbinu mpya za uchambuzi na matumizi yake. Ingawa bado kuna njia ndefu ya kwenda, sekta ya huduma za afya tayari inaona mabadiliko makubwa katika suala la michakato yake na teknolojia zinazotumiwa. Tunaweza tu nadhani jinsi sekta itabadilika katika miaka kumi (au hata tano) lakini kwa sasa, sayansi ya data inaonekana kuwa kadi yake ya trump. Kuna programu nyingi za programu kwenye soko ambazo zinakupa fursa ya kuingiza data katika biashara yako kwa njia rahisi. Pia kuna chaguo la maendeleo ya desturi katika kesi shirika lako ina mahitaji maalum na mahitaji maalum <unk> wasiliana nasi kujifunza zaidi kuhusu kila chaguo na ufumbuzi iwezekanavyo kwamba unaweza kupata.