Improving Healthcare Operations Using Process Data Mining

Editor's Notes

• #2: Do we know what a drug or diagnosis code means and does it mean the same in different EHRs? Similarly, do we know what an EHR event in an EHR event log means and does it mean the same in different systems. This last will be important for comparing process models, as EHRs are so user- customizable. “Check Meds” in one EHR might be called “Medications” in another. What exactly does “Check Meds” mean? Where, exactly, does it fit in a hierarchy of tasks, such as “checking” other things besides medications or involvement of medications in other activities besides “checking”? Is asking a patient about medications (or retrieving the medication list from online) an example of “Check Meds”? Is there a difference in the ordering and frequency of activities between patients that were treated by either a high- or low-volume surgeon? (control-flow perspective) Is there a difference in resource involvement between patients that were treated by either a high- or low-volume surgeon? (organisational perspective) Is there a difference in time-related performance between patients that were treated by either a high- or low-volume surgeon? (performance perspective) Is there a difference in the ordering and frequency of activities between patients that had a throughput time of 80 and 40 minutes or less in respectively the pre-operative and final postoperative examination and patients with a longer throughput time? Is there a difference in time-related performance between patients that had a throughput time of 80 and 40 minutes or less in respectively the pre-operative and final postoperative examination and patients with a longer throughput time? It is apparent that the business processes in the medical domain are dynamic, ad-hoc, unstructured and multi-disciplinary in nature. he goal of clustering is to obtain homogeneous group of patients.
• #11: Subjects, locations, users, different data governance rules and standards that may conflict with each other
• #12: A defining characteristic of modern health care is the rapidly accelerating increase in information that is available to assist with the delivery of care and system management. Time oriented data, 2. High diversity, 3. Some data is functional others are event logs generated by machines. Data came from activities which are part of sequential process Data is timestamped Activities are interdependent discrete events Machine data is generated by many different sources within the healthcare IT infrastructure. These sources include healthcare specific data sources such as electronic health record (EHR) systems, HL7 messaging, and connected medical devices. The data sources include core IT systems that support different applications such as desktops, servers, storage and network devices. Finally, they include all the patient facing applications and systems – portals, billing systems, claim management systems. Machine data generated by this infrastructure shares the core characteristics of big data – lot of data (high volume), created rapidly (high velocity), from different sources (variety), and data that changes over time (variability). Getting timely and relevant insight into this data can be a source of huge value for the healthcare ecosystem.
• #18: Discover actual behavior of people, organization, machines and relate to modeled behavior. Insights showing reality is very different from perceptions, opinions, and beliefs Correlation of Millions of ad-hoc events provide clue for standardization and better prepare to handle ad-hoc events. Different people have different views about a same process. Information about the process mat be incomplete. Discover actual behavior of people, organization, and machines and relate to modeled behavior. Correlate millions of ad-hoc events showing how reality is different from perceptions, opinions, and beliefs. Provide clue for standardization and better prepare to handle ad-hoc events.
• #31: Data Science: validate your assumptions, formulate your hypotheses and test it, find simple principles that may have large impacts and generalized across the population.
• #32: End to End Value stream: Sequential time oriented tasks, resources are dependent on each other, multiple functional disciplines Value each others time and contributions. Any production problems would be quickly detected and corrected.
• #41: One reason for agility is handling of data in scale using parallel data processing techniques. And lastly, we enable operational integration- two ways 1) speed of computations, 2) second is system integration through REST API support.
• #42: Splunk products are being used for data volumes ranging from gigabytes to hundreds of terabytes per day. Splunk software and cloud services reliably collects and indexes machine data, from a single source to tens of thousands of sources. All in real time. Once data is in Splunk Enterprise, you can search, analyze, report on and share insights form your data. The Splunk Enterprise platform is optimized for real-time, low-latency and interactivity, making it easy to explore, analyze and visualize your data. This is described as Operational Intelligence. The insights gained from machine data support a number of use cases and can drive value across your organization. [In North America] Splunk Cloud is available in North America and offers Splunk Enterprise as a cloud-based service – essentially empowering you with Operational Intelligence without any operational effort.
• #54: algorithm=LLB means “bivariate local level”
• #55: Vmware – House of Demos app. VM forest, esx server. Status of VMs when you click on particular one. One of the most useful types of visualizations is a “Sankey diagram”, which is used to describe flows through systems. These can be customer flows through marketing or sales funnels, traffic flows through the actual network, energy flows through a physical system, capital flows through a financial system, etc. It’s a very streamlined form of visualization that cuts out everything unrelated to “flow”. Technically, this is a graph visualization: the nodes are smushed to these bars along the side, and edges are represented by these fat bars connecting nodes. The width of a node is proportional to the volume of flow in and out of the node, and the width of an edge is proportional to the flow from the start node to the end node.
• #56: Customer journey: convert, repeat Mobile Patent Suits Dashed links are resolved suits; green links are licensing. “Thomson Reuters published a rather abysmal infographic showing the "bowl of spaghetti" that is current flurry of patent-related suits in the mobile communications industry. So, inspired by a comment by John Firebaugh, I remade the visualization to better convey the network. That company in the center? Yeah, it's the world's largest, so little wonder it has the most incoming suits.” mbostock’s block #1153292 August 18, 2011 http://bl.ocks.org/mbostock/1153292
• #65: Most concepts in Machine Learning can be organized under two themes: Supervised Learning: finding hidden structure in labeled data Classification: learn labels from labeled data, apply labels to unlabeled data Prediction: guessing future data values given past and current values Estimation: filling in missing data Regression: fitting parameters of statistical models Unsupervised Learning: finding hidden structure in unlabeled data Clustering: grouping together events or rows by similarity Association-Rule Learning: finding relationships in the data Summarization: aggregating and selecting representative values from the data
• #73: Alerts are triggered when certain conditions are met by the results of the search upon which it is based. Alerts can be based on both historical and real-time searches. When an alert is triggered, it performs an alert action. This action can be the sending of the alert information to a designated set of email addresses, or the posting of the alert information to an RSS feed. Alerts can also be set up to run a custom script when they are triggered. You can base these alerts on a wide range of threshold and trend-based scenarios, including empty shopping carts, brute force firewall attacks, and server system errors.
• #80: Is there a difference in the ordering and frequency of activities between patients that were treated by either a high- or low-volume surgeon? (control-flow perspective) Is there a difference in resource involvement between patients that were treated by either a high- or low-volume surgeon? (organisational perspective) Is there a difference in time-related performance between patients that were treated by either a high- or low-volume surgeon? (performance perspective) Is there a difference in the ordering and frequency of activities between patients that had a throughput time of 80 and 40 minutes or less in respectively the pre-operative and final postoperative examination and patients with a longer throughput time? Is there a difference in time-related performance between patients that had a throughput time of 80 and 40 minutes or less in respectively the pre-operative and final postoperative examination and patients with a longer throughput time? It is apparent that the business processes in the medical domain are dynamic, ad-hoc, unstructured and multi-disciplinary in nature. he goal of clustering is to obtain homogeneous group of patients.