{"id":21817,"date":"2015-05-28T14:41:29","date_gmt":"2015-05-28T13:41:29","guid":{"rendered":"http:\/\/www.maxqda.de\/?p=21817"},"modified":"2020-02-03T15:29:22","modified_gmt":"2020-02-03T14:29:22","slug":"lessons-learned-maxqda-und-maxapp-im-einsatz-im-gesundheitswesen-english","status":"publish","type":"post","link":"https:\/\/www.maxqda.com\/de\/blogpost\/lessons-learned-maxqda-und-maxapp-im-einsatz-im-gesundheitswesen-english","title":{"rendered":"Lessons Learned: MAXQDA und MAXApp im Einsatz im Gesundheitswesen (English)"},"content":{"rendered":"<p><em>Dieser Beitrag ist ein Gastbeitrag von Matthew Loxton, Senior Healthcare Analyst bei (WBB Inc). In diesem Artikel stellt er vor, wie er und sein Team MAXQDA und MAXApp im Forschungsalltag nutzen, um &#8222;Lessons Learned&#8220; zu identifizieren und das Knowledge Management ihrer Institution voranzutreiben.<\/em><\/p>\r\n<p><!--more--><\/p>\r\n<h2>The Industry Environment<\/h2>\r\n<p>The healthcare industry is a high-stakes knowledge game. A well-executed good idea can save lives, but a bad idea or a poor execution can cost lives. This is borne out across almost every facet of healthcare from surgical and medical care delivery, to drug design and prescription, to administration, pricing, and service planning. Although healthcare is a highly collegial profession and practitioners are very willing to share ideas, good ideas often move very slowly or remain isolated and underutilized. Truly, the road from Florence Nightingale and Ignatz Semelweiss to today is paved with the gravestones resulting from medical mistakes and missed opportunities.<\/p>\r\n<p><!--more--><\/p>\r\n<h2>Lessons Learned<\/h2>\r\n<p>Knowledge management (KM) includes methods to speed up the process of knowledge consolidation, dissemination, and re-use. One KM method that has proven to be very effective in the healthcare environment is the \u201clessons learned\u201d approach. The lessons learned approach involves using the experiences of clinicians, administrative staff, and management to identify reusable lessons that can assist in replicating good ideas and avoiding previously encountered pitfalls.<\/p>\r\n<figure><img decoding=\"async\" src=\"https:\/\/www.maxqda.com\/de\/wp\/wp-content\/uploads\/Krankenhaus.jpg\" alt=\"Krankenhaus\" width=\"750\" height=\"\" class=\"aligncenter size-full wp-image-19955\" \/><\/figure>\r\n<h2>Qualitative Analysis Team<\/h2>\r\n<p>Our team works as an external facilitator of lessons learned for a large healthcare system. The healthcare system comprises over 150 medical centers that range from small transfusion units and rural clinics to large tertiary hospitals. The lessons learned program exists within a greater quality and safety program that performs measurement and evaluation, and the team focuses on lessons learned related to health information technology (IT), clinical workflow, and administrative processes. The focus remains within the boundaries of cost, safety, and effectiveness of the IT, workflow, or processes, with an emphasis on effectiveness. In short, we want to know what was found to be effective or ineffective, so that subsequent efforts can repeat the effective methods and avoid the pitfalls of the ineffective.<\/p>\r\n<h2>Methodology<\/h2>\r\n<p>The team utilizes mixed methods to examine existing records, interview stakeholders, and synthesize lessons and recommendations. The process typically involves reviewing the organizational goals and their associated metrics, analysis of the related workflow and business processes, and identification of the roles and activities involved. The roles identify the people who are to be interviewed and the topics to be covered, respectively.<\/p>\r\n<p>Prior to site visits or interviews, a literature review is carried out, and is integrated with an existing corpus that aligns with a MAXQDA code system. The corpus includes categorization of stages of application or process implementation, a measurement framework, and a healthcare category structure. <\/p>\r\n<figure><img decoding=\"async\" src=\"https:\/\/www.maxqda.com\/wp\/wp-content\/uploads\/sites\/2\/CodeSystemShadow.png\" alt=\"Wissensmanagement mit MAXQDA\" width=\"301\" height=\"523\" class=\"aligncenter size-full wp-image-20067\" \/><\/figure>\r\n<p align=\"center\"><em>Combined codes from an Suicide Prevention program<\/em><\/p>\r\n<h2>Interview Preparation<\/h2>\r\n<p>Codes that emerge during analysis of the texts are added as Free Codes, and questions and topics that the team wishes to pursue with participants are drawn up within MAXQDA. Questions are developed by coding a text segment, and then adding the questions in a MAXQDA memo attached to the segment. This enables the team to generate a list of questions along with the associated code and text segments. It is easy to keep track of questions that need to be pursued, along with the codes and associated text segments by using the MAXQDA export function and selecting the &#8222;Coded segments with memos&#8220; option.<\/p>\r\n<figure><img decoding=\"async\" src=\"https:\/\/www.maxqda.com\/wp\/wp-content\/uploads\/sites\/2\/Sceenshot-Mathew-Loxton.png\" alt=\"Wissensmanagement mit MAXQDA\" width=\"486\" height=\"264\" class=\"aligncenter size-full wp-image-19997\" \/><\/figure>\r\n<p align=\"center\"><em>Export of coded segments and memos<\/em><\/p>\r\n<p>Once the codes, memos, and associated text segments are exported in csv format, the team is able to tabulate and explore the data by means of Excel pivot tables.<\/p>\r\n<figure><img decoding=\"async\" src=\"https:\/\/www.maxqda.com\/wp\/wp-content\/uploads\/sites\/2\/Codes1.png\" alt=\"Wissensmanagement mit MAXQDA\" width=\"350\" height=\"\" class=\"aligncenter size-full wp-image-19970\" \/><\/figure>\r\n<p align=\"center\"><em>Excel Pivot Table of codes from a clinical program<\/em><\/p>\r\n<h2>Interview Execution<\/h2>\r\n<p>Individual coded segments and associated questions are listed and ticked off unobtrusively during interviews as the participants provide clarifying information. If the participants do not cover the topic in a way that provides an answer without prompting, then the question can be posed directly and the text segment can be used as a context (as required). This has proven to be a highly effective approach, and minimizes the potential for leading the participant. <\/p>\r\n<figure><img decoding=\"async\" src=\"https:\/\/www.maxqda.com\/wp\/wp-content\/uploads\/sites\/2\/Segment.png\" alt=\"Wissensmanagement mit MAXQDA\" width=\"689\" height=\"129\" class=\"aligncenter size-full wp-image-19987\" \/><\/figure>\r\n<p align=\"center\"><em>Question and retrieved segment context<\/em><\/p>\r\n<p>Interviews are carried out at medical centers, or telephonically. While face-to-face interviews are preferred, the travel costs are often prohibitive. The interviews typically explore the participant\u2019s involvement and background, and then step through what the participant felt were examples of effective and notably ineffective methods, and what they believe should be done differently in future.<\/p>\r\n<h2>Data Types<\/h2>\r\n<p>The output of interviews and site visits may comprise any combination of the following forms of qualitative data:<\/p>\r\n<li>Audio files from interviews<\/li>\r\n<li>Video clips of specific processes<\/li>\r\n<li>Still images of work environments, document racks, etc.<\/li>\r\n<li>Hand-written or typed notes by the team members<\/li>\r\n<li>Received documents provided by the participants (either hardcopy or electronic)<\/li>\r\n<h2>Data Analysis<\/h2>\r\n<p>With the increased functionality of smartphones, audio, video, and still images can be pre-coded using <a href=\"https:\/\/www.maxqda.com\/de\/produkte\/maxapp\">MAXApp<\/a>. The pre-coding can be efficiently done between interviews and in transit, where using a PC is impractical. In some cases, typed notes captured on a PC were accessed on an Android tablet via cloud storage. On occasion hand-written notes and hardcopy images have been captured electronically on the smartphone. The MAXApp functionality enabled a considerable contraction in total time taken to code, but also enabled coding to be done while the interviewer\u2019s memory was still fresh.<\/p>\r\n<figure><img decoding=\"async\" src=\"https:\/\/www.maxqda.com\/wp\/wp-content\/uploads\/sites\/2\/mloxton6.png\" alt=\"Wissensmanagement mit MAXQDA\" width=\"450\" height=\"\" class=\"aligncenter size-full wp-image-20058\" \/><\/figure>\r\n<p align=\"center\"><em>MAXApp view of field coding<\/em><\/p>\r\n<h2>Building Lessons<\/h2>\r\n<p>Building the lessons involves a recursive process of reading the documents, watching videos, listening to audio, and reviewing still images from within MAXQDA. Segments are coded as a story of a lesson emerges, and are combined with pre-coded segments in order to build each lesson. Each lesson follows a form of \u201cA caused B\u201d in which an event, action, policy etc. resulted to a specific outcome. For example, a lesson might emerge that a lack of site planning (\u201cA\u201d), resulted in inability to conduct effective training (\u201cB\u201d). A suitable explanatory title is composed, and the context in which it occurred is constructed from the coded segments. These are typically examples of situations at the facility or event in question, as well as examples from the literature review of other facilities or previous events. <\/p>\r\n<p>Each lesson strives to show a clear outcome that is measurable, (or at least identifiable), and addresses a risk, issue, situation, or crisis. Depending on the individual case, the outcome may be either a good or a bad result of the named cause. For example, a lesson may describe that effective planning resulted in a good selection of training environment for a specific health IT application. The lesson would then describe what benefit this had on clinical use, and the context in which the selection occurred. The context may speak to the existence of a good Wi-Fi signal, sufficient lighting, and adequate ventillation for training purposes.<\/p>\r\n<p>In another lesson, the lack of interoperability of electronic health records may be identified as a cause of nurses needing to copy and paste between applications, with a resulting risk that chunks could be accidentally transferred between two different patients. The lessons are used by facility management and project planners to inform policy decisions, and to reduce project risks.<\/p>\r\n<h2>Conclusion<\/h2>\r\n<p>The use of MAXApp and MAXQDA has made the process of data analysis, qualitative data capture, and synthesis of lessons more efficient. Integration of different media types has been made easier, and saves time. Having word count, lexical search, and coding features within MAXQDA enables the team to notice relationships, and to bring disparate content under common codes. As a result, the lessons are brought to bear sooner, and the content quality is improved.<\/p>\r\n<div style=\"background-color:#f3f4f8; margin: 30px; padding:10px; height:230px;\">\r\n<h3 style=\"margin-top:0px;\">About Matthew Loxton<\/h3>\r\n<p><img decoding=\"async\" src=\"https:\/\/www.maxqda.com\/wp\/wp-content\/uploads\/sites\/2\/mloxton.jpg\" alt=\"Wissensmanagement mit MAXQDA\" width=\"150\" height=\"\" class=\"alignleft size-full wp-image-19957\" \/><br\/>This article was written by Matthew Loxton, a Senior Healthcare Analyst with WBB Inc, performing process improvement (PI), quality improvement (QI), and measurement &#038; evaluation (M&#038;E) engagements with healthcare systems and medical facilities. Matthew uses a mixture of Knowledge Management and Lean Six Sigma to improve Health IT deployment and administrative and clinical workflow.<\/div>\r\n","protected":false},"excerpt":{"rendered":"<p>Dieser Beitrag ist ein Gastbeitrag von Matthew Loxton, Senior Healthcare Analyst bei (WBB Inc). In diesem Artikel stellt er vor, wie er und sein Team MAXQDA und MAXApp im Forschungsalltag nutzen, um &#8222;Lessons Learned&#8220; zu identifizieren und das Knowledge Management ihrer Institution voranzutreiben.<\/p>\n","protected":false},"author":1,"featured_media":21818,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","footnotes":""},"categories":[66],"tags":[],"class_list":["post-21817","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-research-projects"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.1 (Yoast SEO v27.1.1) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Lessons Learned: MAXQDA im Gesundheitswesen<\/title>\n<meta name=\"description\" content=\"Beitrag von Matthew Loxton \u00fcber die Nutzung von MAXQDA und MAXApp f\u00fcr die qualitative Analyse und &quot;Lessons Learned&quot; im Gesundheitswesen\" \/>\n<meta name=\"robots\" content=\"noindex, follow\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Lessons Learned: MAXQDA und MAXApp im Einsatz im Gesundheitswesen (English)\" \/>\n<meta property=\"og:description\" content=\"Dieser Beitrag ist ein Gastbeitrag von Matthew Loxton, Senior Healthcare Analyst bei (WBB Inc). 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