Previously, we have spoken about digital pen technology and how it can assist with managing and maintaining patient records. The Nursing Technology Fund is designed to allow healthcare organisations supported by the NHS to apply for a grant to invest in technology that would directly help improve patient care. Read more about the fund here and here.
The most useful and appropriate way to improve patient care is to efficiently and effectively record and learn from patients experiences. Patient experience should be collected using a methodology that will attain the most engagement from patients as the more data collected the more the data will reliably represent the experience at a particular hospital. Additionally, it is important for data capture to be accurate and results to be reported in a way that produces actionable insights for the hospital.
Patients situated at a hospital will be suffering from a number of ailments and are therefore limited in various ways. It is impossible to implement a single solution as part of a patient experience project that will cater to all patients; invariably hospitals will need to use multiple solutions. ‘Multiple solutions’ does not mean buying everything that is out there as part of a hugely inappropriate and expensive patient experience project, it represents using a selection of the relevant technologies and creating bespoke form designs within them to reach out to all patients.
Tablets are an excellent choice for those patients that are unable to walk. One particular trust employed volunteers to visit a patient’s bedside with a tablet to assist them in completing the surveys. Kiosks also play an important part of a well-rounded solution if appropriately located at specific points in hospitals to cater to patients who are able to walk and can complete a survey upon discharge. Additionally, we cannot forget the benefits of paper, living in a world where people are living longer henceforth a larger population of the elderly who prefer traditional methods.
Design should be bespoke to cater to different patients. It is not as complex as it may sound. The use of different types of images and graphics can alone go towards making it easier for patients to complete surveys. For example, pictures appropriate for kids to help ‘gamify’ the process of completing the form or pictures that help the elderly or those with hearing difficulties understand questions i.e. the use of smiley and sad faces in helping a patient understand the question and the options available to answer the question. The use of videos has a similar effect. Similarly, using other forms of media such as audio and animations can help, for those with sight, reading and understanding difficulties. Additionally, making text size, colour and language bespoke are beneficial for flexibility.
Accuracy and Capture
Once technology and design is completed, the next step involved is the back end of the form design. Validations and business rules are the make-up of the back end design that ensures data collected is accurate. The rules and validations encode fields with certain limitations related to character limits and formats as well as compulsory/optional questions covering simple validations. More complex validations and rules are employable as appropriate.
The subsequent step is to accurately capture the data. The capture of data from online/mobile devices such as tablets and kiosks is relatively straightforward. Whilst a patient is completing an online patient experience form they will be made aware of any breach in validation or business rule and are unable to complete the survey until this has been corrected. Therefore, upon clicking submit, the data on the form is accurate and then sent to the desired destination
On the other hand paper forms enter DCC’s extensive extraction, verification and quality check process to ensure data is accurate, anomalies are eliminated and appropriate changes implemented when notified of a break in validation or business rule. It is after this stage, data is exported to the pre-defined location.
Now that the raw data is present, free from inaccuracies it is vital to analyse and process this data in a way made actionable.
As explained here an interactive dashboard possesses many benefits namely its graphical, bespoke interface, its flexibility with any type of data, compatibility with endless number of systems and analysis capabilities.
However, natural language processing is growing in prominence. Let’s take a look at the Clarabridge engine. The Clarabridge NLP engine rates feedback from free text data on an 11 point negative to positive scale. The Clarabridge NLP engine is able to understand context and identify grammatical modifiers, for example: “the nurse was friendly” will get a rating of +1 “the nurse was really friendly” will increase the rating to +3 as the word “really” amplified the positive sentiment a patient felt towards the nurse. Additionally, the engine can identify different themes from large amounts of free text and give them all a sentiment rating individually and provide an overview. For example, in a hospital a patient may comment on staff, bathroom facilities, food quality, cleanliness and so forth. The engine will give you the sentiment on each of the factors a patient has mentioned and a sentiment on the hospital overall, made up of these individual elements. The advantages of such a mechanism are that it allows a hospital to see exactly where they can implement improvements allowing them to make positive changes.
The image below displays a typical Clarabridge reporting tool; the top left quadrant is a bar chart measuring patient feedback categories (categories mentioned by patients in their feedback) against data volumes (amount of time mentioned), which allow a hospital to understand the most heavily talked about topics. Top right is the word relationship cloud. Rather than forming the word cloud from a count of words, the cloud shows the relationship between words for added accuracy. The colour of the word relationships depicts sentiment, while the size of the word reflects volume. Bottom right quadrant is a double-axis chart, measuring volume of topics mentioned when giving feedback vs sentiment felt towards those topics. Bottom left is a heat map again displaying the main factors mentioned. Colour depicts sentiment and size depicts volume.
© 2013 Clarabridge, Inc.
Contact us today to learn more about text and sentiment analytics and its application and the Nursing Technology Fund. Alternatively, book your free educational knowledge share workshop. (What is a workshop?)