Navigating Healthcare Data Warehouse Challenges: Common Development Issues & Mitigation
What is a Healthcare Data Warehouse?
The basic definition is quite simple- “A healthcare data warehouse is a central repository of data that is specifically designed to support decision-making processes within the healthcare sector.”. But one needs to delve deeper to understand the various attributes of a well-designed healthcare data warehouse. Healthcare data is perhaps the most diverse and complex. While health data ecosystems are maturing, initiatives for generation, storage, integration, and usage of high-quality data are still very fragmented. What adds to this complexity is the fact that healthcare data no longer sits in one establishment. As we move from episodic hospital-based care to proactive and chronic patient management practiced in several places such as clinics, communities, or at home, it’s a struggle for this industry to bring complex and disparate data together while still maintaining security, privacy, and governance. Given the volatile nature of healthcare data, the industry needs an elastic, adaptive, hyper-connected, and intelligent data warehouse that combines features of a traditional data warehouse, clinical repository, and health information systems, with new operating models governing the scale and speed of such an architecture.
Challenges Associated with Healthcare Data Warehouse:
Finarb has implemented secure data warehouses for its healthcare clients, and from our practical experience, we are highlighting some of the challenges organizations often face in their healthcare data warehousing journey and what are the critical success factors to keep in mind while designing and implementing a high-performing data warehouse.
1. Creating a Logical Data Model with only a Technology point of view
Building a logical data model is probably the cornerstone of a successful healthcare data warehouse because it serves as the blueprint for the physical data models and the final warehouse. An ideal logical data model combines business requirements and quality data structure, the two most vital basics, into a diagrammatic representation. If logical data models are not thought through from a business perspective, it can lead to incorrect physical data models and poorly conceived data warehouses. Taking the time to set clear business goals, and establish what datasets are needed, can go a long way. Some of the common mistakes with respect to logical models are listed below -
Building the logical model with only a technology PoV, rather than an integrated (technical & business) PoV:
Analyzing the current business objective, and future goals of building the data model and hence the healthcare data warehouse, should be central. This helps in discerning data pertinent to the specific business goals. This can be especially challenging and time-consuming in healthcare – the industry deals with a blend of structured (e.g., transactional & master tables) and unstructured (e.g., clinical notes in free text, audio, image, video) data sources, high volume and velocity of multi-dimensional data such as patient records, lab reports, genomic sequences, radiographic images, and electronic medical notes.
Mitigation measures: It's always good to build a sound business data model or a conceptual data model before you move to the logical data model. Align your healthcare organization goals with the kind of clinical data you want to store in the data warehouse. For instance, identify data needed to perform regular business processes or enterprise operations and data needed for longer-term business KPI measurement in the healthcare data warehouse. Define each of these entities and attributes and the relationship between each entity. A sound conceptual data model will have a significant impact on the time-to-value of your data warehouse going forward.
Wrong levels of granularity in the logical modeling stage, can severely bring down the performance of your healthcare data warehouse:
In the realm of healthcare, selecting the appropriate level of data granularity is pivotal. This concept pertains to the depth of detail available within our data. Striking the right balance is imperative. While an excess of granularity could inundate business users who might seek a holistic overview, conversely, insufficient granularity hampers the ability to extract nuanced insights.
Mitigation measures: Collaborate with business users, domain experts, to determine the level of detail that meets their analytical needs for the data warehouse. Map the available data sources and assess the granularity at which data is collected in the healthcare data warehouse. Consider the types of analysis that will be performed on the data and hence levels of granularity requirement. At the same time it's important to take into account factors like data volume, storage space, and performance needs while strategizing on the granularity for it. Clearly document the rationale behind the chosen granularity levels to ensure alignment with business goals and for future reference.
Not planning upfront for Derived or Calculated Fields:
Not all fields are found in the hard data. There are many instances where derived, or calculated values will be necessary for future analytics in the data warehouse. Not planning for these derived values in your data modeling stage can be a costly mistake, which can slow down and make future query processes cumbersome. Furthermore, it can cause major inconsistencies and impair your analytics in the data warehouse going forward.
Mitigation measures: Planning derived values in collaboration with business departments is key for the data warehouse. If there are calculated values that will be consistently used for reporting or analysis, it's advisable to perform these calculations during the ETL process and store them beforehand. If there are complex formulas that require significant computational resources, it's recommended to save the resulting derived value.
A data model not designed for scalability:
As healthcare data warehouses grow, they face challenges due to increased data volume, velocity, and variety. The importance of a logical data model design that incorporates aspects of scalability cannot be emphasized enough. Sometimes even the best data models that look good theoretically, fail miserably when faced with increased data loads over time in the data warehouse. Some common factors contributing to such inefficiencies are wide partitions due to improper partition key selection, and poorly conceived indexes leading to cumbersome query processing.
Mitigation strategies: Estimating the number of values that will be stored in a partition can be the first step toward mitigating this challenge for the healthcare data warehouse. Choosing an even distribution for the partition key, considering horizontal sharding for large tables, and selecting indexes to optimize query performance in the modeling stage can also help mitigate any risks related to scalability of your data warehouse.
2. Data Integration from Disparate Healthcare Datasets- a Multifaceted Problem
Healthcare datasets are very diverse in nature, originating from various sources. Integrating data from disparate sources can be quite a challenge to merge heterogeneous healthcare datasets into one single unified healthcare data warehouse. This requires a suite of carefully curated data solutions encompassing data cleansing, restructuring, storing, and integration.
Diverse datasets can lead to integration challenges compromising the integrity of your data warehouse. Some of the common challenges faced during data integration and mitigation measures:
Data from various sources/in different formats with different schemas, each with its own sets of segregation, management, and integration issues:
The diversity of data sources, from Electronic Health Records (EHRs) and laboratory systems to imaging systems and wearable devices, introduces a multitude of data formats. During the ETL (Extract, Transform, Load) process, each data format demands its specific extraction technique. Integrating data from disparate sources during this phase is essential. Ensuring data quality and uniformity necessitates potentially unique transformation logic. Complications may arise from misinterpretations, such as incorrect handling of HL7 fields or overlooking the de-identification of sensitive patient data. Such oversights can jeopardize the quality and usability of the healthcare data warehouse. Also, XML data could be housed in specialized XML databases like eXist, while structured EHR data in relational databases like PostgreSQL. Due to varied data sources, schemas diverge, leading to differing numbers of data tables and subsequently necessitating intricate query and analytical methodologies for the data warehouse.
Mitigation Strategy: Start with meticulously analyzing data and its formats, identifying the essential transformations. Maintain uniformity by setting data levels consistent with the specific use case, emphasized by a profound comprehension of said use case. Implement a robust ETL platform proficient in handling a gamut of clinical data sources, ranging from databases and Excel files to CSVs and XMLs. Integrating data from disparate sources is key here. During transformation, deploy schema mapping and validation checks, ensuring data integrity remains uncompromised in the data warehouse. To further streamline this intricate process, consider adopting a Data Lake architecture first. This permits the storage of raw data in its original format, which can then be transformed into a unified format within the data warehouse.
The emergence of new data types can pose a threat to healthcare dataset schema:
Data schemas in healthcare data warehouses are subject to change as new data types emerge (like genomics data). Any changes to the data warehouse schema must be meticulously managed to prevent data corruption, loss, or misinterpretation, all of which can jeopardize patient care and data security.
Mitigation Strategy: Document any schema modifications using a version control system like Git. Incorporate Continuous Integration/Continuous Deployment (CI/CD) pipelines for rigorous testing and smooth deployment of schema changes.
Metadata structure can become quite complex and difficult to manage due to schema heterogeneity:
Healthcare data sources often employ heterogeneous schemas, resulting in a variety of metadata structures and representations in the healthcare data warehouse. This metadata, which encompasses data dictionaries, taxonomies, ontologies, and lineage information, is crucial for the accurate interpretation and semantic understanding of clinical information in the data warehouse. A lack of metadata normalization or standardization can compromise data integrity, leading to potential clinical decision misjudgments. Moreover, inconsistent metadata handling can complicate the true nature or classification of data, posing risks of unintended data disclosure in the data warehouse, especially if de-identification processes rely on these metadata descriptors.
Mitigation Strategy: Implement a comprehensive metadata management platform, designed to handle polymorphic metadata structures and schemas across diverse healthcare datasets in the healthcare data warehouse. Utilize an enterprise-grade metadata registry that employs canonical data models and schema cross-walking techniques for metadata harmonization and validation. Adopt attribute-based access control (ABAC) and data-centric encryption techniques to ensure robust protection and granular access to sensitive metadata attributes.
3. Improper data cleaning can damage data sanctity, undermining analytical precision in downstream operations:
Erroneous data cleansing best practices introduce statistical biases into healthcare databases, leading to the degradation of the integrity of stored data vectors. Such perturbations in data structures can misguide advanced analytical algorithms, adversely affecting their predictive accuracy and inference reliability. Within the framework of healthcare data warehouses, these distortions can cascade, culminating in suboptimal patient care decision models. Consequently, preserving the sanctity of data at the preprocessing phase becomes paramount to ensure robust, outcome-driven clinical analytics in the data warehouse.
The inability to correctly supersede missing data can lead to inaccurate deductions:
The assortment of healthcare data introduces significant issues when handling ambiguous or missing data in the healthcare data warehouse. NULLs in datasets are cryptic: they could signify a myriad of scenarios ranging from unrecorded, not applicable, to genuine missing data. Automated algorithms, when deployed inappropriately, can introduce biases, especially when datasets have non-random missing patterns, leading to erroneous conclusions in the data warehouse.
Mitigation Strategy: Lean on advanced imputation techniques. Machine Learning-based imputers, such as Deep Learning models, can be trained to predict missing values by leveraging patterns from large amounts of data in the data warehouse. Data cleansing best practices involve model-based imputation methods like MICE (Multiple Imputation by Chained Equations) which can be utilized, treating each variable with missing values as a function of other variables. Post-imputation, utilize goodness-of-fit tests and Residual Analysis to validate imputed values in the data warehouse. Consider maintaining a delta-log of changes post-cleaning, and implement real-time monitoring using tools like Apache Kafka, alerting on anomalies detected by unsupervised learning models such as One-Class SVM or Isolation Forest within the framework of data warehouses.
Data existing in unstructured formats could lead to computational difficulties:
Data from various unstructured sources like medical imaging interpretations, clinical transcripts, handwritten notes, patient-provider emails, wearable device outputs, genomic data, medical literature, social media feedback, and audio-video recordings could be quite difficult to manage in the healthcare data warehouse due to a lack of schema, a clear structure, and pre-defined attributes. Cleaning this data poses a lot of risks as well - there is potential for loss of critical data nuances during standardization based on data cleansing best practices, unintended exposure of sensitive patient information could breach data privacy standards in the data warehouse, incorrect processing might introduce biases or inaccuracies, etc.
Mitigation Strategy: Implementing standardization protocols ensures uniformity across diverse data sources in the healthcare data warehouse. Advanced Natural Language Processing (NLP) tools can efficiently process and convert clinical transcripts and other text data into structured formats for data warehouses. Integration middleware aids in reconciling data mismatches, ensuring seamless amalgamation of varied data types. Automated compliance monitoring tools continuously validate data processing activities against healthcare regulations in the data warehouse.
4. Inadequate Data Governance could be a costly and time-consuming mistake:
Data governance in a healthcare data warehouse ensures the accuracy, consistency, and security of patient data, safeguarding patients' well-being and trust. It ensures compliance with regulatory requirements, reducing the risk of legal repercussions and financial penalties. Additionally, effective governance enhances decision-making by providing reliable and standardized data for healthcare analytics and insights in the healthcare data warehouse.
The absence of a well-documented lineage can pose a threat to data integrity:
In healthcare, ensuring traceability in data transformations, from initial cleaning to integration into a unified model in the data warehouse, is pivotal. This approach upholds data governance in data warehouse in healthcare, ensuring transparency and integrity throughout data handling. However, a key challenge often faced, is the occasional absence of documented lineage in source files, necessitating advanced computational methods to rectify the gaps in the data warehouse.
Mitigation Strategy: data lineage can be handled by adopting SCD-2, where new data is inserted and updated with a specific time column and a flag as reference. This helps in keeping track of the historical changes. If you are dealing with transactional data, try adopting an intermediate storage (Data Lake) or archive (Gen 1 storage, local drives, servers etc..) to reduce the storage cost and increase search efficiency in the healthcare data warehouse.
Lacking clear performance metrics to track your healthcare data warehouse efficiency, can hinder optimization:
Effective data warehousing hinges on specific performance metrics. It's vital to measure query response time to know how quickly results are fetched. Data load latency is crucial, indicating the time from when new data surfaces to its incorporation in the data warehouse. The system's ability to handle multiple users or concurrency levels should be monitored to avoid performance drops. Additionally, data storage efficiency, achieved through optimal data formats and compression, cannot be overlooked. Incorporating these benchmarks in governance is essential for streamlined and efficient operations of your healthcare data warehouse.
Mitigation Strategy: Have clear and measurable performance metrics of your data warehouse sketched out during the data modeling stage itself. Deploying robust Business Intelligence (BI) tools like Tableau or Power BI can help visualize and pinpoint performance anomalies, facilitating early intervention in the data warehouse. Next, leveraging Data Warehouse Automation (DWA) solutions such as WhereScape or Talend can automate aspects of the warehouse lifecycle, enhancing speed and accuracy. Implementing Data Management Platforms (DMPs) like Informatica can address data load latencies, ensuring timely and efficient ingestion in the data warehouse. For managing user concurrency, tools like Apache Kafka can be utilized, providing real-time data handling even under high user load in the healthcare data warehouse.
Inadequate Data Testing compromises integrity, compliance, and operational efficiency in healthcare data warehousing:
Improper data testing can compromise data integrity; unchecked inconsistencies might distort data representations in reports, leading to misleading analytics. This flaw is further enhanced by potential compliance breaches, where unnoticed data anomalies might result in the storage of Protected Health Information (PHI) in non-compliant ways, risking violations of standards like HIPAA in the healthcare data warehouse. A related issue is decreased traceability, where inadequate testing obscures data lineage, thereby complicating error tracing during audits and regulatory checks. The efficiency of the ETL processes might also be compromised due to inadequate data validation and suboptimal transformation logic, resulting in redundant data processing and misaligned data structures. Moreover, if backup processes aren't tested thoroughly, there's a risk that some critical datasets aren't captured or that recovery mechanisms malfunction during crucial moments.
Mitigation Strategy: First and foremost, adopting a CI/CD framework can ensure data is consistently tested and validated before being integrated into the primary systems of the data warehouse. Healthcare quality frameworks, with built-in anomaly detection mechanisms, should be integrated to monitor and correct discrepancies, ensuring report accuracy and preventing misleading analytics.
Ungoverned Data Archive and Data Redundancy can pose legal risks:
Many healthcare organizations focus on new data but forget old, archived data in the data warehouse. Ignoring this can lead to legal issues, privacy breaches, and data errors. It's tough to manage because healthcare data is vast and often repetitive. Having redundant data in the system poses a risk of increasing storage costs. Having said that, organizations must ensure that the data is cleaned without harming data quality in any way. While saving on storage costs, it's crucial to keep data ready for quick access, especially in cases of high risk patients. Wrong moves can increase costs, break laws, and delay vital health info access in the healthcare data warehouse.
Mitigation Strategy: Incorporating an advanced Data Lifecycle Management (DLM) system ensures not only the proper aging of data but also the adherence to compliance standards throughout the data's lifecycle in the data warehouse.
5: Lack of robust encryption of PHI and PII in cloud-based healthcare data warehousing
When an organization moves to cloud for its data warehousing, the PHI and PII information has to be secured before we move the data to cloud or before pre-processing and at the time of data movement from different zones to the final data warehouse. It is important to make sure data is secure and not accessible for anyone.
Figure 2: General encryption and decryption flow for Healthcare PHI data
Inadequate encryption practices in healthcare data warehousing risk compromising sensitive patient data and violating HIPAA regulations:
Data encryption is of paramount importance when it comes to healthcare data warehousing due to the critical nature of the information being stored. Healthcare data often includes sensitive and private information, such as patient medical histories, diagnoses, treatments, and personal identifiers. Encryption becomes particularly crucial during data warehousing, where large volumes of data are stored in centralized repositories. In the event of a breach or unauthorized access, encrypted data remains incomprehensible, ensuring that even if the physical storage is compromised, the data itself remains protected. Encryption is vital for PHI data in healthcare due to the need to comply with stringent privacy regulations like HIPAA, which mandates data protection to maintain patient confidentiality.
Mitigation Strategy: Enforcing strong data encryption methods by utilizing the standard encryption algorithms, such as AES for symmetric encryption or RSA for asymmetric encryption, to convert data into an unreadable format. This ensures that unauthorized individuals cannot access the information without the appropriate decryption keys. By implementing strong encryption, choosing appropriate encryption modes, managing keys securely, enforcing access controls, and utilizing Transport Layer Security (TLS) for data transmission, healthcare organizations can prevent unauthorized access during data transmission and storage. Regular key rotation, adherence to encryption standards and regulations, strong authentication methods, and security audits further bolster the effectiveness of encryption measures. Encryption serves as a pivotal aspect of a comprehensive security strategy that safeguards PHI data, preventing potential breaches and maintaining the trust of patients and regulatory bodies alike.
Unauthorized access to encryption keys elevate risks of privacy breaches:
Unauthorized access to healthcare data and encryption keys can have severe consequences, including compromising patient privacy, exposing sensitive medical information, and potentially leading to identity theft or fraudulent medical activities. Such breaches undermine patient trust, can result in legal penalties for healthcare providers, and may disrupt critical medical services, ultimately jeopardizing patient well-being and the integrity of the healthcare system. Often encryption keys are given directly to the users which may lead to potential breach in future.
Mitigation strategy: Implementing robust access controls, role-based permissions, and encryption technologies ensures authorized personnel-only access to sensitive healthcare data. Regular security audits and vulnerability assessments identify and rectify system weaknesses. Multi-factor authentication strengthens authentication mechanisms, deterring unauthorized access. Employee training fosters security awareness and thwarts social engineering attacks. Employing column-level encryption keys, stored securely alongside a master key, enhances data protection. Robust network security, including private endpoints and multi-level access controls, fortifies against breaches. By integrating these strategies, healthcare entities can effectively minimize risks tied to unauthorized data access and encryption key breaches.