使用sitecore实现医疗保健超个性化的正确方法

Safely use patient data to deliver unique and engaging experiences If you're a healthcare provider or payer, tailoring treatments, communications,and services to fit individual needs and preferences is no longer a luxury — it’sessential. Hyper-personalization goes beyond traditional personalization methods(which rely on broad user segments) by using real-time data, predictive analytics,contextual awareness, and personalized communication to deliver highlyindividualized experiences. In this ebook, we share the right approach to achievehyper-personalization. We'll begin by looking at the four elements required forhyper-personalization and what can be accomplished. 4 ESSENTIAL ELEMENTS FORHYPER-PERSONALIZATION 1REAL-TIME DATA For a patient with diabetes, a mobile app connected toa continuous glucose monitor (CGM) provides real-timeblood sugar readings. The app captures and displaysdata for the patient and connects with their healthcareprovider's system. If blood sugar levels go out of saferange, the app alerts both the patient and their careteam. The app also gives personalized advice, such asadjusting diet or insulin doses based on glucosereadings. Patients can message or video chat with theirprovider, creating a smooth digital experience. Thiskeeps patients engaged in their care and allows thehealthcare team to intervene quickly when needed. Healthcare organizations captureand use the latest patientinformation, such as health recordsand treatment plans, to anticipateissues and respond quickly andaccurately to their needs. 2PREDICTIVE ANALYTICS For example, AI-driven predictive models help preventhospital readmissions for heart failure patients. Byanalyzing historical data from thousands of patients —age, gender, medical history, lab results, vital signs, andpast admissions — an ML algorithm identifies thoseat the highest risk of readmission within 30 days ofdischarge. The system looks for patterns linked to readmissions,such as weight gain, blood pressure changes, or issueswith medication adherence. Healthcare providers thenstep up with support, like home health monitoring,personalized follow-up plans, or medicationadjustments. AI and machine learning (ML) helpforecast patient behaviors andoutcomes using historical data,identify at-risk patients, and offerprompt, proactive interventions. In this example, a high-risk patient might receive apersonalized care plan through a mobile health appor patient portal. This app reminds them to takemedications, log symptoms, and check vital signsdaily. It may also connect them to a nurse or carecoordinator who is alerted to any unusual patient datain real time and reaches out if concerning trends arise. 3CONTEXTUAL AWARENESS A good example is a personalized care managementplatform for patients with chronic conditions likeasthma. An asthma management app uses contextualawareness by integrating patient demographics,health status, and environmental factors, such aslocal air quality. For someone living in a city with high pollution, wherepoor air quality might trigger asthma attacks, theapp pulls real-time air quality data and combines itwith the patient’s health information, including theirhistory of asthma attacks and medication adherence.If the air quality is poor, the app sends a notificationrecommending that the patient stay indoors, takepreventive medication, or avoid strenuous outdooractivities. It also reminds them to bring their inhalerwhen they leave home. Contextual awareness considersthe patient's environmentand circumstances, such asdemographics, health status,and personal preferences. Thisapproach ensures that care isappropriate and effective. The app also considers personal preferences for alerts,such as SMS, email, or in-app notifications, ensuringthat care advice is received promptly through theirpreferred channel. 4PERSONALIZED COMMUNICATION Consider a scenario where smart insulin pumps fordiabetes management are paired with a personalizedmobile app. A patient using the pump automaticallyadjusts insulin delivery based on glucose levels. Theapp also provides real-time data and tailored insights.It uses the patient's medical history, daily routines, andpreferences to customize communication. For example, if the app notes past glucose patterns,it might remind the patient to check blood sugar aftermeals or before exercise. If the patient prefers fewernotifications during work hours, the app adjusts itsalerts and communicates in the preferred languageand tone — whether through concise alerts or detailededucational messages. Personalized communicationmakes every message andpatient interaction meaningful.It resonates based on personalpreferences and context, fosteringdeeper engagement and trust. When glucose levels trend high or low, the app offerspersonalized advice, suggesting specific insulin dosageadjustments or dietary recommendations. CASE STUDY:ENTERPRISE-LEVELSOLUTION TO ENHANCEPATIENT EXPERIENCES Consider this real-life example of tu