Clinicians drown in documentation, from justifying their treatment decisions to charting their compliance with the latest protocols – even in feeding electronic records systems. Once fed, these systems make it difficult to retrieve data important to clinical decision-making. Often in healthcare, too many technologies build barriers instead of bridges to finding crucial information and turning it into actions that help patients.
Most industries survive and thrive on receiving the right data at the right time, organized and visualized in the most impactful manner, and increasingly on any device, at any time, in any place. Many of their systems, however, are parochial, reflecting the needs of one business unit above others. These systems are ossified, older technologies that are expensive to adapt to rapidly changing business environments.
Perhaps most concerning: many systems are passive, presenting the needed snapshots, shares and trends, but requiring the user to traverse menus and search for often disparate components to make connections, draw key inferences and determine how to turn those data into action. Time is precious, and even in many progressive organizations, far too much is wasted in gathering, assimilating and interpreting critical data.
It doesn’t have to be that way. Just as Amazon can suggest what to buy before you realize you need it, so too can technologies learn from collected data over time, finding clusters, trends and outliers that command attention. This tech can bypass the morass of menus and dashboards and access just what’s needed, delivering directly to you only the actionable highlights.
Imagine this: a physician is about to see a patient. In addition to the traditional lists of health information such as diagnoses and known medications, she sees bulleted text generated by a technology that highlights key lab trends. From this information, she is aware of the typical variation for this patient and alerted if certain results are outside of limits that she previously defined.
Conversing with the patient reminds her of a diagnostic procedure. Rather than hunting through the electronic medical record (EMR), she texts the EMR’s database to “find latest date for test X,” and to “show results trend for text X,” receiving the date, graph and statistics on her phone or tablet. She decides that she wants to monitor this condition closely, so she creates two key performance indicators (KPIs) on her personal dashboard, instructing one to notify her if the value of test X exceeds a value specific to this patient and instructing the other to notify her if the value of test Y goes outside the normal variation for this particular patient. These notifications can be sent to the chart, her email or even to her phone.
Because procedures require pre-authorization, another text ‘bot’ culls from the EMR, returning all the necessary information in a simplified bullet-point list to a nurse from the health plan, who can use this information to make her determination. The use of such bots will eliminate the need for office staff to waste countless hours hunting through dozens of digital pages for the proper health records.
Pulse8 is always seeking new ways to maximize efficiency with emerging technologies, and with our two latest developments – Natural Language Generation (NLG) and Chatbots – we are making the aforementioned scenario in the healthcare space a reality. NLG uses machine learning to deliver an analytic narrative and generate a storyline instantly, while Chatbots take it a step further and engage in a two-way conversation with the user about the analytics in question.
These developments provide real-time results delivered where needed and in an action-enabling manner. Technologies that provide these insights and efficiencies exist today and their thoughtful application by forward-thinking technology vendors, such as Pulse8, could save enormous amounts of time while supplying truly personalized patient monitoring.
To learn more about how Pulse8 can benefit you, visit: http://www.pulse8.com/