{"id":4881,"date":"2021-01-14T02:43:13","date_gmt":"2021-01-14T02:43:13","guid":{"rendered":"http:\/\/orionhealth-blog.local\/?p=4881"},"modified":"2024-06-07T03:13:31","modified_gmt":"2024-06-07T03:13:31","slug":"the-challenge-making-sense-of-data","status":"publish","type":"post","link":"https:\/\/orionhealth.com\/au\/blog\/the-challenge-making-sense-of-data\/","title":{"rendered":"The challenge: Making sense of data"},"content":{"rendered":"<h4 class=\"wp-block-heading\"><strong>Deploy analytics tools <\/strong><\/h4>\n\n\n\n<p>In the first blog of this series, we discussed the importance of aggregating data to provide a complete view of the patient drawn from across multiple sources, wherever the patient has received care. Almost as soon as any data has been aggregated, a range of analytic tools can be used to help advance the quality of care and improve clinician-patient interactions.  <\/p>\n\n\n\n<p>One of the simpler approaches is to analyse population-level quality measures such as the number and identity of patients with diabetes and abnormal A1c levels, or patients with diabetes who\u2019ve not had an eye check. The ability to have both a population-level score, as well as immediate linkages to the individual patients involved, is key to enabling real-time improvements in care.   <\/p>\n\n\n\n<p>Quality scores are provided in great numbers and detail by both clinical governing entities as well as other organizations such as the National Committee for Quality Assurance (NCQA) in the US and the National Institute for Health and Care Excellence (NICE) in the UK. For\u202fongoing healthcare quality\u202freporting purposes, it is essential to have analytics\u202fand reporting tools that\u202fare automatically updated\u202fto meet\u202fthe latest\u202fmeasures. <\/p>\n\n\n\n<p>In addition to providing quality measures, tools that enable healthcare providers and population-level managers\u202fto mine and explore whole-population and specific cohort-level data are key. To deliver advanced population health\u202fcare, analytic tools need to be employed to explore data sets\u202fefficiently, discover meaningful insights and identify gaps in care at both a population and individual level.\u202f <\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Explore data in-depth, discover insights, identify gaps in care <\/h4>\n\n\n\n<p>Soon, as a much broader range of data elements become integrated into the patient record (including social determinants of health (SDOH), behavioural, patient-generated data, and genomic data) there will be a corresponding demand for analytic tools to mine that data. To make sense of the vast volume of healthcare data now available to clinicians and population health managers, we need to employ next-generation\u202ftools that enable\u202fpopulation\u202fexploration in detail according to a broad range of\u202ffactors.  <\/p>\n\n\n\n<p>For example, if a healthcare provider is interested in investigating\u202fand improving\u202fthe\u202fimmunisation rate\u202fin their jurisdiction, population exploration would involve first understanding the overall\u202fimmunisation\u202frate. Simply determining that the rate is low is not enough to develop action plans to address the problem. In this way, the quality measures need to be followed by exploring the data looking for causative factors. Is it that children\u202fof minority groups are less likely to be vaccinated? Are\u202flow-income families a concern? Or perhaps the low rate is due to vaccine refusal typically associated with upper middle-income groups? <\/p>\n\n\n\n<h4 class=\"wp-block-heading\">Predictive modelling using artificial intelligence and machine learning <\/h4>\n\n\n\n<p>So far,\u202fwe have been completing the analysis in near real-time. Even so, it is \u2018after the event\u2019 -\u202fafter\u202fthe patient was\u202for was not\u202fvaccinated on schedule, for instance. What if we could look ahead and predict with some degree of accuracy the patients who, in the next time period, will receive a vaccination and who won\u2019t?\u202f What about finding patients in a population who are both early-stage pregnancy and at high risk for poor outcomes? If we could see more clearly into the future, clinicians and managers would have truly powerful tools to improve the quality of care and outcomes. To do this requires\u202fpredictive modelling\u202fusing\u202fartificial\u202fintelligence and\u202fmachine learning algorithms\u202fapplied to\u202faggregated,\u202fhigh-quality data\u202fin a population.\u202f\u202f <\/p>\n\n\n\n<p>It&#8217;s an ambitious undertaking to have high-quality data aggregated in real-time and available for a range of analysis including quality measures, exploration of data and application of predictive modelling. Nevertheless, it is achievable with the technology available today, with improved population health as the major benefit. <\/p>\n\n\n\n<p><em>Interested in learning how Orion Health can help you to make sense of your data? <\/em><\/p>\n\n\n\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/orionhealth.com\/au\/white-papers\/a-path-to-advanced-population-health\/\">Read the whitepaper<\/a><\/div>\n\n\n\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/orionhealth.com\/au\/contact\/\">Contact an expert<\/a><\/div>","protected":false},"excerpt":{"rendered":"<p>Deploy analytics tools In the first blog of this series, we discussed the importance of aggregating data to provide a complete view of the patient drawn from across multiple sources, wherever the patient has received care. Almost as soon as any data has been aggregated, a range of analytic tools can be used to help [&hellip;]<\/p>\n","protected":false},"author":35,"featured_media":4882,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[1],"tags":[],"region":[25,23,24],"class_list":["post-4881","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","region-au","region-global","region-nz"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The challenge: Making sense of data - Orion Health<\/title>\n<meta name=\"description\" content=\"Leveraging\u202fdata-driven intelligence to improve care delivery is also something that a\u202fsound\u202ftechnology architecture\u202fcan\u202ffacilitate.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/orionhealth.com\/au\/blog\/the-challenge-making-sense-of-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The challenge: Making sense of data - 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