Electronic health records(EHRs) capture longitudinal multi-visit patient journeys but are difficult to analyze due to temporal irregularity, multimorbidity, and heterogeneous coding. Thisstudy introduces a temporal and comorbidity-aware trajectory representation thatrestructures admissions into ordered symbolic visitstateswhile preserving diagnostic progression,secondary comorbidities, procedure categories, demographics, outcomes, and inter-visit intervals. These symbolic states are subsequently encoded as fixed-length numerical vectorssuitable for computational analysis. Validationwas conducted in two stages: Stage I assessed construction fidelity using coverage metrics, comorbidity preservation, diagnostic transition structures, and exact inter-visit gap encoding and Stage II assessed analytical utility through clustering experiments using different clustering approacheslike sequence similarity, Gaussian Mixture Models(GMM), and a temporal LSTM autoencoder(TS-LSTM). Proof of conceptwas done by encoding subset of patient cohortsfrom the MIMIC-IVdatabase consisting of 2,280 patientswith 8,849 admissions having complete primary diagnosis coverage and near-complete secondary coverage. Stage 1 assessment consisting of cohortlevel coverage metrics confirmed that the transformation preserved essential clinical information and key properties of longitudinal EHRs.In Stage 2, clustering experiments validated the analytical utility of the representation acrosssequence-based, Gaussian mixture, and temporal LSTM autoencoder approaches. Ablation studiesfurther demonstrated that both multimorbidity depth and inter-visit gap encoding are critical to maintaining clusterseparability and temporal fidelity. The findingsshowthat explicit encoding of comorbidity and timing improvesinterpretability and subgroup coherence.Although evaluated on a single dataset, the use ofstandardised ICD-10 EHR structure supportsthe assumption that the framework can generalise across healthcare settings; futureworkwill incorporate multimodal data and external validation.

Traditional healthcare information systems (HISs) store patient information in their own clinics or hospitals in a decentralized manner. These healthcare systems rarely communicate with one another. Communication between health information systems has immense potential to provide personalized care based on historical patient information and customize individual treatments. The integration of patients with healthcare experts and their sharing of health information across healthcare systems prior to diagnosis helps enhance the quality of care by providing better treatment plans and recommendations. The effective use of personalized care with large language models plays a crucial role in making HISs more intelligent and improving the quality of care. This chapter aims to identify the challenges associated with implementing personalized care in LLMs and provide research directions for overcoming these obstacles and enhancing the effectiveness of personalized care in these populations.

The shift from paper-based health records to Electronic Health Records (EHR) resulted in a vast volume of digital patient information. The knowledge derived from this data can be used for better decision-making and im-proving Personalized Care. It is very challenging to analyze EHR due to its high dimensional and heterogeneous nature. Deep Learning techniques can be used in this scenario. This work proposes a personalized care framework using Patient Similarity (DeepPCPS). DeepPCPS incorporates autoencoders for dimensionality reduction and uses various techniques for deducing patient similarity to find clusters o f similar patients. Experiments were conducted with the Jaccard index and the Sorensen Dice similarity index technique to calculate simi-larity scores. Based on similarity score, K-means, DBSCAN and agglomerative clustering algorithms were applied to form patient cohorts. The result suggests that the Sorensen Dice similarity with K-means has better results than the Jaccard index similarity. A quantitative analysis is conducted to support the claim. Qualitative analysis was conducted on the clusters formed from Sorensen similarity to show how the cohorts were used for customized treatment. The results obtained were promising, and further investigation is needed by incorporating additional treatment and timeline features for personalized care.

Electronic Health Records (EHR) contain rich information about patients that can be used to improve decision support systems that assist Health Care workers. Personalized care is a concept that is gaining much acceleration as it talks about tailoring the treatment of an individual by proactively identifying possible risks. One approach used in personalized care is to identify similar patients to plan customized treatment. This paper reports work done to identify patient similarity from EHR records to form clusters of similar patients. Paper discusses the proof of concept that uses Jaccard Similarity and the Sorensen-Dice Similarity Index to find patient similarity. The similarity score calculated is used to build clusters that were optimized using the Elbow Method and Silhouette Index. The quantitative analysis of metrics suggests that Jaccard Index with Silhouette Index and Sorensen-Dice Similarity Index with the Elbow Method gave better clusters. The EHR of the patients in a cluster can be used to derive insights that can guide personalized care.

Today almost all hospitals have adopted Electronic Health Records(EHR), and the digital data that is captured provides an opportunity to improve the patient care. In addition to the EHR in the healthcare system, data such as the readings from smart wearable devices worn by patients also give valuable insight to the patient's health condition. Hence there is a need to consolidate all these heterogeneous information into EHR of patients to get a holistic view of the patient. Currently, different healthcare systems and devices operate in different standards which in turn causes interoperability issues. This paper explores the need to achieve semantic interoperability among healthcare data and proposes a technique to derive inference from semantically annotated EHR data. A proof of concept is done to validate the proposed design.

Healthcare system is fast evolving with the digitization of health information. Electronic health record (EHR) is generated from various sources like hospitals, laboratories, mobile devices, IoT, and social media. EHR data collected for a person cumulatively give information about patient’s health history. There need to be schemes to transfer EHR data among different healthcare providers in a secure and efficient manner to provide better quality healthcare experience. Interoperability allows transferring information among various stakeholders in the medical domain. Syntactic interoperability allows transferring data among different systems, whereas semantic interoperability enables different systems to interpret meaningfully and accurately exchange data. This chapter builds the case for semantic interoperable EHR systems that enable the healthcare systems to work together and achieve better patient outcomes and effective healthcare. Semantic interoperable systems with artificial intelligence, big data analytics, and data mining help in making real-time decisions for better care. It also reviews the current work, benefits, and challenges in the area of semantically interoperable EHR systems and highlights research direction for the same.