SnoMAP: Pioneering the Path for Clinical Coding to Improve Patient - PDF document

SnoMAP: Pioneering the Path for Clinical Coding to Improve Patient Care Michael LAWLEY a , Donna TRURAN a , David HANSEN a , Norm GOOD a,, Andrew Staib b , Clair Sullivan b a Australian eHealth Research Centre, CSIRO b Princess Alexandra Hospital, Clinical Excellence Division Queensland Health Abstract. The increasing demand for healthcare and the static resources available necessitate data driven improvements in healthcare at large scale. The SnoMAP tool was rapidly developed to provide an automated solution that transforms and maps clinician-entered data to provide data which is fit for both administrative and clinical purposes. Accuracy of data mapping was maintained. Introduction The healthcare system is undergoing rapid digital transformation. The initial primary driver for this digitisation of health care delivery is increased efficiency and quality at the point of patient care. However, increasingly clinicians and system managers are seeing the potential for secondary use of the clinical data collected drive health system improvements. Increasing demand for healthcare in the face of static resources has reinforced this need for digital solutions enabling data driven decision making in healthcare. Australia has only recently delivered its first tertiary digital hospital with an integrated electronic medical record (EMR)[1]. During the rollout of this EMR however, it became clear that Australia has a clinical coding dilemma. The rich clinical data coded by the clinicians did not meet administrative coding requirements for government funding of the hospital. The decision had been made at a state level to use a clinically useful code set for the EMR (Systematised Nomenclature of Medicine: Clinical Terms, Australian Extension (SNOMED CT-AU)) but the government required reporting of this data in a different, administratively useful, code set (International Statistical Classification of Diseases and Related Problems, Australian Modification (ICD-10-AM)). In order to retain funding for the new digital hospital, a strategy had to be developed to rapidly and accurately transform the clinically useful code set (SNOMED CT) into a different administratively appropriate code set (ICD-10-AM).The discrepancy between clinician-entered SNOMED codes and administratively required ICD codes for an inpatient stay could be dealt with by clinical coders manually entering ICD codes based on clinical information in the EMR. This was the planned mitigation strategy for situations such as inpatient hospital admissions where manual coding from the paper record already existed. However, not all hospital attendances were subject to manual entry of clinical codes by professional coders. There are over 1.5 million attendances to Emergency Departments per year in Queensland[2]. Clinical coding for these attendances was entered by clinicians at the point of care, and submitted directly to the central agencies as part of a minimum dataset for performance and financial [3-5]. Either an additional, manual coding step or a tool that allowed rapid, accurate mapping of the full range of SNOMED codes in use into ICD codes was required. Existing tools were inadequate due to lack of code coverage and accuracy, so we had to rapidly develop a solution. This aim of this paper is to: 1. Technical Brief:  define the current tension between clinically useful data sets and administrative data sets  provide a detailed description of the tool we delivered 2. Describe the implementation processes locally and across other sites

3. Outline the clinical care impact  describe a post hoc analysis showing funding levels and accuracy were maintained after transforming the dataset. Technology Brief Without a rapidly developed solution, the hospital could potentially face funding cuts. The timeline was short with the entire development occurring over four months. The brief was to create an automated solution which transforms and mapped clinician-entered data to provide data which was fit for both administrative and clinical purposes. The tension between datasets In Australia, the coding systems most widely used in health services are ICD-10-AM[6] and SNOMED CT- AU [6 7]. ICD-10-AM is a statistical classification designed for encoding information about inpatient episodes of care[7]. It is not suitable for use by clinicians, at the point of care or for clinical record documentation[8]; its categories often combine multiple conditions into a single code so that the specific condition is unknown. The statistical nature of ICD-10-AM encoded data best suits population health surveillance (morbidity and mortality)[7 9-11], and as a basis for financial modelling and resource utilisation[12 13]. ICD-10-AM is the mandated coding system for secondary data collections such as the Admitted Patient Care National Minimum DataSet [4] and the Non-Admitted Patient Emergency Department National Minimum Dataset[4]. SNOMED is an international standardised, multilingual vocabulary of clinical terminology that is designed for use by physicians and other health care providers to document patient medical records at the point of care in all healthcare settings. SNOMED CT includes synonyms and definitions with unique identifiers capable of being electronically exchanged between healthcare providers. SNOMED provides clinically relevant, very specific, and descriptive terms suited to care delivery. SNOMED encoded data collected at the point of care can be analysed, aggregated and re-used for multiple purposes [14 15], including safety and quality of care reviews and performance metrics. The Australian extension provides local variations and customisations of terms relevant to the Australian healthcare community. These two coding systems are different, intended for different user groups, and for different purposes. One does not replace the other but both must operate in harmony within the health information eco-system, where both clinical and statistical data must be accurate and meaningful and reflect the patient, clinician and health service experiences[16]. Existing Mapping Systems Mapping has long been regarded as the most effective way of allowing SNOMED to be implemented in EMRs while co-existing in a health information administrative environment that has traditionally used ICD-10-AM [15]. The maps previously developed for use in ED settings (2009) were inadequate, did not reflect the terminology preferences of clinician users, were not maintained and updated, and did not perform under Activity Based Funding protocols[17]. New comprehensive, version-controlled maps were required to allow PAH data to be mapped, audited and submitted in each monthly reporting cycle. Table 1 provides a description of the maps made available for this case study and Table 2 compares the previously released maps which cover only a subset of SNOMED concepts – those belonging to the Emergency Department Reference Set (EDRS) [15]. Table 1 AEHRC maps between SNOMED CT AU and ICD-10-AM 9 th edition (2014) ICD-10-AM 9 th edition 2014 Reference Set SNOMED CT AU Concepts 79, 639 10,018 Descriptions/synonyms 1,414,009 na