{'fraction_non_alphanumeric': 0.025841816758026624, 'fraction_numerical': 0.010180109631949883, 'mean_word_length': 5.834224598930481, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Practical Approach to Governance and Optimization of Structured Data Elements

Definition and configuration of clinical content in an enterprise-wide electronic health record (EHR) implementation is highly complex. Sharing of data definitions across applications within an EHR implementation project may be constrained by practical limitations, including time, tools, and expertise. However, maintaining rigor in an approach to data governance is important for sustainability and consistency. With this understanding, we have defined a practical approach for governance of structured data elements to optimize data definitions given limited resources. This approach includes a 10 step process: 1) identification of clinical topics, 2) creation of draft reference models for clinical topics, 3) scoring of downstream data needs for clinical topics, 4) prioritization of clinical topics, 5) validation of reference models for clinical topics, and 6) calculation of gap analyses of EHR compared against reference model, 7) communication of validated reference models across project members, 8) requested revisions to EHR based on gap analysis, 9) evaluation of usage of reference models across project, and 10) Monitoring for new evidence requiring revisions to reference model.