{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:43:28Z","timestamp":1785545008456,"version":"3.56.0"},"reference-count":18,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Population health management requires tools that transform complex clinical data into actionable insights to guide care coordination, community outreach, and system-level planning.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The objective of this study is to develop and apply a population health intelligence dashboard that integrates inpatient utilization, process indicators, and health status data for patients with diabetes mellitus, using a clinically meaningful classification system and geospatial visualization.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n We used data from the SingHealth Diabetes Registry (SDR; 2019\u20102024) to build an interactive dashboard using R Shiny (Posit Software). A semiautomated mapping algorithm was developed to map\n ICD-10-AM<\/jats:italic>\n (\n International Classification of Diseases, 10th Revision, Australian Modification<\/jats:italic>\n ) principal diagnosis codes into CCSR (Clinical Classifications Software Refined) categories. We built an interactive dashboard in R Shiny incorporating 3 analytic domains: inpatient utilization (by admission count, length of stay, and prolonged stays), diabetes care process indicators, and health status indicators (eg, comorbidities, laboratory results, and diabetes-related complications). Geographic information system mapping enabled spatial visualization by patients\u2019 residential locations.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Diabetes mellitus with complication (END003) was the leading cause of admission (7.0%\u20108.1% annually), followed by pneumonia (RSP002, 3.7%\u20105.1%), fluid and electrolyte disorders (END011, 3.4%\u20104.1%), and skin infections (SKN001, 2.8%\u20103.1%). In 2024, top\n ICD-10-AM<\/jats:italic>\n diagnoses under END003 included E1122\u2014type 2 diabetes mellitus with established diabetic nephropathy, E1173\u2014type 2 diabetes mellitus with foot ulcer due to multiple causes, and E1172\u2014type 2 diabetes mellitus with features of insulin resistance. For END011, the most frequent diagnosis codes were E877\u2014fluid overload, R18\u2014ascites, E875\u2014hyperkalemia, and hypo-osmolality and hyponatremia. In total, SingHealth Diabetes Registry patients accounted for 687,062 inpatient bed days in 2024. Circulatory conditions (eg, cerebral infarction and heart failure) contributed 124,417 (17.7%) bed days, while injuries (eg, hip fractures and surgical complications) accounted for 86,541 (12.6%) bed days. CCSR-based analyses revealed distinct patterns when comparing conditions driving admission frequency versus prolonged length of stay. GIS mapping identified residential clusters with high inpatient utilization, unmet care processes, and poor cardiometabolic control, supporting region-specific intervention planning.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The dashboard demonstrates a novel, interactive approach to visualizing inpatient utilization, care gaps, and health status, enabling targeted, place-based interventions. It represents a scalable framework for operationalizing population health intelligence across other chronic disease areas and health care systems.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/80431","type":"journal-article","created":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T14:55:08Z","timestamp":1781189708000},"page":"e80431-e80431","source":"Crossref","is-referenced-by-count":1,"title":["Integrating Clinical Classifications Software Refined, Process Indicators, and Geographic Information System Mapping to Inform Population Health Management: Development of an Interactive Dashboard"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4518-0477","authenticated-orcid":false,"given":"Joshua Kuan","family":"Tan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8904-8936","authenticated-orcid":false,"given":"Hao Yi","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8795-7491","authenticated-orcid":false,"given":"Gerald Gui Ren","family":"Sng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7591-3141","authenticated-orcid":false,"given":"Sing Yi","family":"Chia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3758-9982","authenticated-orcid":false,"given":"Su-Yen","family":"Goh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6712-5535","authenticated-orcid":false,"given":"Julian","family":"Thumboo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5482-2646","authenticated-orcid":false,"given":"Yong Mong","family":"Bee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,7,15]]},"reference":[{"key":"R1","doi-asserted-by":"crossref","unstructured":"Greenes R Del Fiol G , editors. Clinical Decision Support and beyond: Progress and Opportunities in Knowledge-Enhanced Health and Healthcare. 3rd ed. Academic Press; 2023. [doi: 10.1016\/B978-0-323-91200-6.00005-X] ISBN: 9780323912006","DOI":"10.1016\/B978-0-323-91200-6.00005-X"},{"issue":"1","key":"R2","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1089\/pop.2015.0149","article-title":"Defining population health management: a scoping review of the literature","volume":"20","author":"Steenkamer","journal-title":"Popul Health Manag"},{"issue":"3","key":"R3","doi-asserted-by":"publisher","DOI":"10.2196\/34894","article-title":"Dashboards in health care settings: protocol for a scoping review","volume":"11","author":"Helminski","journal-title":"JMIR Res Protoc"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.2147\/CLEP.S300663","article-title":"Establishment of the SingHealth diabetes registry","volume":"13","author":"Lim","journal-title":"Clin Epidemiol"},{"issue":"10","key":"R5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0275920","article-title":"Trends in diabetes-related complications in Singapore, 2013-2020: a registry-based study","volume":"17","author":"Tan","journal-title":"PLoS One"},{"key":"R6","unstructured":"Type 2 diabetes mellitus\u2014personalising management with non-insulin medications. Agency for Care Effectiveness (ACE); 2023. URL: https:\/\/isomer-user-content.by.gov.sg\/68\/ec113034-4200-4aed-af2d-2516b5f79293\/acg-t2dm-personalising-medications.pdf [Accessed 16-06-2026]"},{"key":"R7","unstructured":"Clinical classifications software refined (CCSR) for ICD-10-CM diagnoses. Healthcare Cost and Utilization Project (HCUP). URL: https:\/\/hcup-us.ahrq.gov\/toolssoftware\/ccsr\/ccs_refined.jsp [Accessed 11-04-2025]"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2024.105508","article-title":"Development and external validation of tools for categorizing diagnosis codes in international hospital data","volume":"189","author":"Malecki","journal-title":"Int J Med Inform"},{"key":"R9","unstructured":"OneMap API Documentation. OneMap Singapore. URL: www.onemap.gov.sg\/apidocs\/ [Accessed 14-04-2025]"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.2196\/59828","article-title":"Development, implementation, and evaluation methods for dashboards in health care: scoping review","volume":"12","author":"Helminski","journal-title":"JMIR Med Inform"},{"issue":"1","key":"R11","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-024-17841-2","article-title":"Developing public health surveillance dashboards: a scoping review on the design principles","volume":"24","author":"Rabiei","journal-title":"BMC Public Health"},{"issue":"4","key":"R12","doi-asserted-by":"publisher","DOI":"10.1071\/PU24029","article-title":"Development of a digital dashboard to address population-level obesity using electronic medical record data","volume":"35","author":"Canfell","journal-title":"Public Health Res Pract"},{"issue":"3","key":"R13","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1016\/j.cardfail.2023.08.024","article-title":"Development and optimization of the Veterans Affairs\u2019 national heart failure dashboard for population health management","volume":"30","author":"Brownell","journal-title":"J Card Fail"},{"key":"R14","unstructured":"Vaccinations. Communicable Diseases Agency (CDA). URL: https:\/\/www.cda.gov.sg\/public\/vaccinations [Accessed 16-04-2025]"},{"issue":"1","key":"R15","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-023-09957-9","article-title":"Challenges in predicting future high-cost patients for care management interventions","volume":"23","author":"Crowley","journal-title":"BMC Health Serv Res"},{"key":"R16","doi-asserted-by":"publisher","DOI":"10.2196\/58463","article-title":"Machine learning-based prediction for high health care utilizers by using a multi-institutional diabetes registry: model training and evaluation","volume":"3","author":"Tan","journal-title":"JMIR AI"},{"key":"R17","unstructured":"White paper on Healthier SG. Ministry of Health, Singapore; 2022. URL: https:\/\/file.go.gov.sg\/healthiersg-whitepaper-pdf.pdf [Accessed 25-01-2024]"},{"key":"R18","unstructured":"Data sharing for healthier SG. Healthier SG. URL: https:\/\/www.healthiersg.gov.sg\/enrolment\/data-sharing\/ [Accessed 07-02-2026]"}],"container-title":["JMIR Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/medinform.jmir.org\/2026\/1\/e80431","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T22:26:41Z","timestamp":1785536801000},"score":1,"resource":{"primary":{"URL":"https:\/\/medinform.jmir.org\/2026\/1\/e80431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,15]]},"references-count":18,"URL":"https:\/\/doi.org\/10.2196\/80431","relation":{},"ISSN":["2291-9694"],"issn-type":[{"value":"2291-9694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,15]]},"article-number":"v14i1e80431"}}