AI is not about creepy robots creating assembly-line healthcare. It is about systems that assist and support the wisdom and experience of well-trained clinicians in making better data-driven decisions and taking actions that best support the needs of those they serve. It does this by gathering and crunching massive amounts of data quickly and intelligently to identify patterns often overlooked or undiscovered in the traditional practice of care. The opportunity for AI in healthcare isn’t just about making doctors and healthcare providers more efficient in their work; it’s about making the lives of the patients better and saving lives. <click>
Today’s health organizations need solutions that can help them engage patients in their health <click>, empower care teams <click>, optimize clinical operations <click>, transform the care continuum <click> and enhance security and compliance.
Transition: AI can help you proactively engage your patients in their health.
Improve the patient experience throughout the care journey by empowering care teams with streamlined, 360-degree interactions. Through modern, patient-centered communications and efficient processes, health organizations can increase satisfaction and save time for patients and staff alike, while lowering costs. <click>
Enable intelligent tools to recommend next best actions for individual care plans Reaching an accurate diagnosis can sometimes be a challenge even for the most skilled providers. However, the mass digitization of patient data via EMR and EHR systems opens the door for AI and machine learning to help augment the diagnostic process to build effective individual care plans. Through analysis of the patient’s digital health footprint, such as lab results, history and reported symptoms, advanced analytics tools can surface potential diagnoses and recommend the next best actions for care.
Customer Story: https://customers.microsoft.com/en-US/story/delivering-data-to-doctors-to-enable-the-best-decisions-at-the-point-of-care Demonstration: (link) Products: Text Analysis API, Translator Text API, Recommendations API, ML Server
Engage digital assistants to record and transcribe patient history and chart notations Dictation tools have come a long way since the Dictaphone, and it’s not uncommon to find some type of dictation tool packaged into EMR or EHR systems. However, voice-to-text powered by AI is transforming the way providers can transcribe patient histories and chart notations. With blazingly fast voice-to-text and instant translation capabilities, AI can provide vital time-saving assistance to busy providers on-the-go, and across any device.
Customer Story: (link) Demonstration: (link) Products: Translator Text API, Language Understanding Intelligent Service (LUIS) <click>
Provide remote patient monitoring while applying analytics to generate care team alerts Remote monitoring via connected devices has changed the way that patients interact with their care teams. The ability to track biometrics with sensors allows patients to rest in the comfort of their own home, yet remain in a provider’s constant care. AI tools can further enhance the remote monitoring process by engaging chatbots to push notifications directly to care teams based on defined biometric triggers, enabling them to respond in real-time to changes in a patient’s status.
Customer Story: https://customers.microsoft.com/en-US/story/zionchina Demonstration: (link) Products: Bot Framework, Text Analytics API, Translator Text API, Azure IoT Suite
Transition: With AI solutions, you take steps to further optimize your organization for effectiveness.
Today’s AI technology has made massive leaps forward, and few industries have felt that more keenly than healthcare. Continued investment in AI technology is enabling modern health organizations to transform the care continuum in ways they never thought possible. <click>
Enable virtual nursing assistants to remotely assess patient symptoms and decrease unnecessary visits When seeking medical care, patients are often required to see their provider in person for even minor concerns. A digital health organization can help lessen the number of unnecessary patient visits through AI. Using natural language, nursing assistant chatbots can interact with the patient and help assess their symptoms by asking decision-tree questions such as “Do you have a fever?” or “Are you coughing?” The chatbot can then route the conversation directly to a care provider or create a live telehealth session in order for the provider to make a determination on next steps for the patient. Cognitive capabilities can further expand virtual nursing services by providing real-time translation localized to where the patient lives.
Customer Story: (link) Demonstration: (link) Products: Bot Framework, Text Analysis API, Translator Text API, Bing Speech API, Computer Vision API, Recommendations API, ML Server
Employ advanced learning models to expedite the medical imaging workflow and identify potential findings Today, one of the biggest problems facing physicians and clinicians in general is the overload of too much patient information to sift through. This is where AI can play a key role. AI will not be diagnosing patients and replacing doctors — it will be augmenting their ability to find the key, relevant data they need to care for a patient and present it in a concise, easily digestible format. When a radiologist calls up a scan to read, the AI will review the image and identify potential findings immediately—from the image and also by combing through the patient history related to the particular anatomy scanned—in order to suggest possible findings.
Customer Story: (link) Demonstration: (link) Products: Text Analysis API, Computer Vision API, Recommendations API, ML Server
Combine cognitive robotic capabilities with medical records to guide and enhance physician instrument precision Cognitive robotics is still a newer field, but its advancing rapidly, and the impact to surgical teams could be significant. Cognitive robots use machine learning and other forms of AI, and can integrate information from pre-op medical records with real-time operating metrics to physically guide and enhance the physician’s instrument precision. The technology incorporates data from actual surgical experiences to inform new, improved techniques and insights. Using cognitive robotics can result not only in better outcomes but also reduce the length of patient hospital stays for health organizations.
Customer Story: (link) Demonstration: (link) Products: Vision API, Text Analytics API, Entity Linking API, ML Server
Transition: AI solutions can strengthen trust in security and compliance across the care continuum
次世代のデータ活用としてのIntelligent Data Platform－Microsoft Azure と AI (Artificial Intelligence)
Service Azure Functions
R & Python ベースの
AI の ストアドプロシージャ
MicrosoftML Library の組み込み
• SQL Server, CNTK & R/Python – それぞれの強い部分を連携させた
エンタープライズ グレードの AI アプリケーション
• GPU による、処理能力の向上
Diagnosis: 35% certainty
Python / R で実装した ストアドプロシージャ ストアド
New Images table
@language = N'R'
, @script = N'
x <- as.matrix(InputDataSet);
y <- array(dim1:dim2);
OutputDataSet <- as.data.frame(x %*% y);'
, @input_data_1 = N'SELECT [Col1] from MyData;'
, @params = N'@dim1 int, @dim2 int'
, @dim1 = 12, @dim2 = 15
WITH RESULT SETS (([Col1] int, [Col2] int, [Col3] int, [Col4] int));
‘R’ もしくは ‘Python’
R file や Python file の
入力データ。 T-SQL SELECT も使
トレーニング済みのモデルには varbinary(max) を
Result set のバインド(Optional)
STDOUT や STDERR と一緒に
R dataframe もしくは
Python Pandas dataframe
それぞれの SQL インスタ
MSSQLSERVER Service MSSQLLAUNCHPAD Service
“何を” そして “どう”
Run your “query”
In-memory OLTPColumn Store
Data Process Training Deploy Prediction