Big data is being increasingly applied to improve patient experience and outcome globally. pengalaman pesakit dan hasil telah sebahagian besarnya diabaikan setakat ini terutamanya kerana hospital dipercayai bahawa ia diperlukan untuk menyambung tidak emosi dan objektif dengan pesakit dan merawat penyakit,,en,Tetapi hospital menyedari bahawa rawatan pesakit melampaui penyakit fizikal,,en,Ia adalah mengenai bagaimana pesakit emosi merasakan tentang beberapa perkara seperti tingkah laku,,en,ekspresi wajah,,en,approachability,,en,kebersihan,,en,pengalaman bil dan empati berpengalaman dalam hospital,,en,Faktor-faktor tersebut merupakan pengalaman pesakit dan hospital cuba untuk mengira pengalaman pesakit dalam bentuk data yang besar,,en,Beberapa kes penggunaan memberi keterangan kepada keberkesanan data yang besar dalam meningkatkan pengalaman pesakit dan hasil,,en,Apakah pengalaman pesakit dan hasil bermakna,,en. But the hospitals are realizing that patient treatment goes beyond physical ailments. It is about how the patient emotionally feels about several things such as behavior, facial expression, approachability, cleanliness, billing experience and empathy experienced in the hospital. Such factors constitute patient experience and hospitals are trying to quantify patient experience in the form of big data. Several use cases testify to the effectiveness of big data in improving patient experience and outcome.
What does patient experience and outcome mean?
A patient’s experience with a hospital is not only connected to the quality of pure medical treatment but also with the overall emotional experience that includes, but may not be limited to ease of appointments, approachability and friendliness of staff, timeliness, empathy and cleanliness. Patients can award scores based on his or her overall emotional experience with the hospital. Big data technology is able to capture the emotional experience in the form of data. The image below shows that the broad range of emotions and experiences a patient can have is captured in the form of data in various mediums such as social media sites.
Patient experience
The data about patient experience can be sourced from different sources, as shown in the image below.
Data source
Given below are five powerful ways big data is being used to improve the patient experience and outcome.
The Cleveland Clinic way
The Cleveland Clinic significantly improved its patient experience with the help of analytics. Back in 2009, the clinic had not been receiving high scores on patient satisfaction index. Needless to state, that had a lot of implications. So, the CEO Dr. Cosgrove decided to dramatically improve patient services by relying on analytics. To turn the vision into reality, the clinic hired Dr. James Merlino as the Chief Experience Officer. Dr. Merlino hired a third-party agency to conduct a quantitative and qualitative study on what the patients expected from the clinic. The findings from the analytics were different from what the clinic thought the patients expected from the clinic. The patients, the analytics revealed, expected respect, clear and consistent communication and happy hospital staff. These expectations were connected with the emotional state of the patients. Patients wanted display of concern and empathy from hospital staff. It is significant that the study by the outside firm could quantitatively capture patients’ feelings and deliver them as analytics.
Gaining insights from big data
Gaining insights is the first important step towards improving patient experience and there are a lot of ways to do that. Pertama sekali, data needs to be accessed from the different sources, as shown in the images above. Selepas itu, the data can be analyzed to find out patient feelings towards hospitals. For example, social media and website discussions could reveal that patients tend to feel a lot of anger at the billing inefficiencies of a particular hospital. Advanced analytics could quantify these ranges of emotions. Analytics engines could search websites such as Twitter to find out trending topics on healthcare and analyze the content. It is important to identify the most important issues in the minds of the patients — it could be availability of parking spaces, lack of clarity in communication, unclean bathrooms and even chaotic billing counters.
Identify trending or hot topics
The idea is to identify the trending topics and assign ratings to them to signify the seriousness or importance. For example, positive feedback could be awarded green colors and negative feedback could be given red colors. Advanced analytics is capable of generating reliable ratings from such feedback. Such feedback could also provide valuable materials for the hospitals to create Key Performance Indicators (KPI). The advantage with this approach of gathering and analyzing data is that it happens relatively quickly, when compared to the traditional method of surveys.
Creating action plans and goals
After identifying the trending or hot topics, the next step is to identify a set of variables that are playing a role in patient dissatisfaction. Variables are a set of parameters with assigned values that obviously, change over time. Dalam konteks ini, examples of variables could be billing errors, waiting time at the investigation departments, lack of process, poor attitudes towards patients and difficulty in taking appointments. After the variables are identified, the hospital can decide on what constitutes an acceptable change in variable values. For example, the hospital could target to limit billing errors to 1% of the total number of bills generated in a month. Data science is also capable of reasonably estimating the impact of the change in variable values on patient experience.
Reducing readmissions
According to Paul Muller, Chief Software Evangelist at HP, hospital admissions in the United States constitute about 30% of the total annual healthcare cost and 20% of all hospital admissions happen within 30 days of discharge. Muller observed, “In other words, we’re potentially letting people go without having completely resolved their issues. Better utilizing big-data technology can have a very real impact, contohnya, on the healthcare outcomes of your loved ones.” Obviously, reducing hospital readmissions could significantly improve patient outcome. But how could big data potentially help reduce readmissions? The key lies in accessing and analyzing medical and health data of the patients and developing plans accordingly. If a patient is readmitted within 30 hari, something has most probably gone wrong with the post-discharge care. So, accurate analysis needs to be done on the possible risks, actions, emergency situations, medications, history of illnesses and so on.
Reducing avoidable expenses
According to Muller, medical errors are one of the biggest contributors to the avoidable medical expenses in the United States which could be as high as 17.6% of the GDP. kesilapan perubatan seperti span yang tinggal dalam perut selepas pembedahan atau berlebihan yang membawa kepada jangkitan boleh memandu hospital kos menanggung dan perbelanjaan insurans,,en,Ketidakcekapan dalam proses juga secara tidak langsung menyumbang kepada kos yang lebih tinggi,,en,Ini pergi tanpa mengatakan bahawa ketidakcekapan dan kesilapan boleh memberi sumbangan besar kepada rasa tidak puas hati pesakit dan mengurangkan skor hasil pesakit,,en,analisis data besar,,en,digabungkan dengan teknologi yang betul,,en,boleh mendedahkan masalah dengan cara yang objektif,,en,Kematian akibat kesilapan dan kecuaian boleh memandu pesakit dari hospital yang,,en,analisis betul boleh mengawal perpindahan yang,,en,pengalaman pesakit dan hasil telah diabaikan komponen rawatan untuk masa yang lama dan ia akhirnya mendapat perhatian yang sewajarnya,,en. Inefficiencies in the process also indirectly contribute to higher costs. This goes without saying that inefficiencies and errors can contribute substantially to patient dissatisfaction and reduce patient outcome scores. Big data analytics, combined with the right technology, can expose the problems in an objective manner. Deaths due to errors and negligence can drive patients away from a hospital. Right analysis can control the churn.
Summary
Patient experience and outcome have been neglected components of treatment for a long time and it is finally getting the attention it deserves. data yang besar yang paling mungkin adalah cara terbaik untuk menangani isu ini,,en,hospital juga perlu mengambil kira perspektif terlalu banyak bergantung kepada data,,en,yang terbaik,,en,boleh memberitahu hanya sebahagian daripada cerita,,en,Cabarannya adalah untuk mendapatkan perbuatan berserta,,en,dengan meletakkan pelan tindakan yang dijana daripada data dalam amalan,,en,Terdapat perspektif lain juga,,en,boleh pelaburan dalam analisis data besar juga memandu sehingga bil hospital,,en,Yang boleh menjadi satu lagi bidang pengalaman pesakit,,en,Ia akan menjadi menarik untuk melihat bagaimana hospital bermain perbuatan mengimbangi,,en. However, hospitals also need to consider the perspective of too much reliance on data. Statistics, at its best, can tell only a part of the story. The challenge is to getting its act together, by putting the action plan generated from the data in practice. There is another perspective also: can the investment in big data analytics also drive up hospital bills? That can be another area of patient experience. It will be interesting to see how hospitals play the balancing act.
