How can healthcare organizations use data to improve service?

My take: Consider Epistemology Be cognisant of, reflect on and make explicit my/your/our underlying philosophical assumptions about the nature and role of data. For example: Is data ‘the truth’? or is it simply one perspective? Is it to be used to judge? or simply to nudge? Why do this? Because the rest of my planning around manipulating data for service improvement is based on my subconscious presuppositions about the nature of data. If I don’t reflect on those underlying assumptions, my subsequent logical arguments are in essence, constructed on quicksand. This is often why folk have heated non-resolvable arguments. Why? Because their assumptions about the nature and purposes of data are housed on different philosophical planets.

Most healthcare organizations serve a community in a local setting and their success depends upon how often patients continue to avail the services for their healthcare needs. Hence, it becomes imperative on the part of healthcare organizations to focus on patient experience and ensure that their processes are aligned with this goal. Most patients would measure their experience with a healthcare setup in terms of waiting times, predictability of processes, how receptive is the staff to their needs and how soon can they get relieved from their ailments. Empathy & trust play a major role here and if an organization makes it a part of their team culture then it becomes easier to offer better experience to patients.

I agree and feel that it can also help to analyse outcome of Pt. base on multiple factors which can helpful for research and validation

Healthcare organizations can achieve great deal of credibility by recording and sharing their clinical quality parameters like treatment outcomes, complications and readmission rates. This also directly ensures teams adhere to standard clinical protocols and take adequate care of their patients safety and experience. Hospitals can also go a step further and make their quality protocols and stats like duration of hospital stay and complication rates for different procedures available to the community in simple language. A comparative historical data from representative institutions can help people understand the quality of care being provided.

Unlike traditional businesses, healthcare organizations have to ensure availability of operational staff 24x7. This puts a lot of pressure upon their finances and operational efficiency as patients care cannot be compromised. Data analytics can help expose hidden insights and can help improve operational efficiency of hospitals tremendously. Analysis of admissions, case mix and seasonal variations in disease incidence can help hospitals predict operational requirements beforehand so that resources allocated accordingly. This helps avoid staff shortages and wastage of time sensitive resources can be minimized. Historical data can also help plan clinical staff requirement, equipment availability and streamline nursing & housekeeping services.

In my opinion, one of the key challenges in healthcare and health promotion is, how the public sector enables timely targeted preventive measures for those those who would potentially benefit most from timely interventions. In these cases, preventive interventions can be implemented in a customer-oriented and cost-effective manner.

Harnessing Artificial Intelligence tools to efficiently leverage data can significantly contribute to the advancement of precision medicine. The two pivotal datasets in this context are: 1. Medical/Treatment History: Comprehensive medical and treatment records, encompassing diagnostic history and medication usage throughout a person's life. 2. Genomic Data: Genomic data derived from gene sequencing. The proficient analysis of these datasets plays a substantial role in the early and accurate detection of rare and challenging medical conditions, leading to significant positive outcomes.

Population Health incorporates two concepts: (a) health outcomes for a defined population, and (b) the equitable distribution of outcomes within that population. Both parts are equally important and it is not unusual (as above) to emphasize (a) over (b). Data is the critical currency to enable measurement and drive improvement in both areas. Value-based payment models offer tools to drive desired outcomes in a population such as better diabetes or hypertension control. The second part however encourages us to look upstream at the whole person, challenges us to recognize that needs for care may vary across a population, and it is within an individual or community context that a pathway to a better health will emerge.

This does a great job bringing to light the overarching concept which is improving health for all (along with all of the complexities, like equity, that go along with it). However, it misses a key point - that after the data helps us identify the needs, risks and disparities, we need to focus on eliminating barriers to getting care. In The 2022 Healthcare Consumer Experience Index by The Harris Poll, there are 2 quotes that tell us data is not the only answer. 1) Nearly half (49%) of consumers say that “in some cases, I stopped trying to make an appointment with my healthcare provider because scheduling became too difficult.” 2) 67% of people agree that "Right now, it feels like every step of the healthcare process is a chore"

Data from healthcare organizations can greatly influence policies and community health planning especially in a resource limited setting. Hospital data from different locations has been vastly helpful in creating population level strategies and policies around disease control for conditions like malaria, COVID-19, dengue, MMR, IMR and noninfectious conditions like cancer, diabetes and cardiac conditions. Collation of population data has been backbone of designing healthcare policies and programs and continues to influence how we respond to newer challenges with human health.

Data analytics helps healthcare organizations and societies in many ways- 1. Disease notification. 2. Geographical occurrence patterns 3. Insights around disease causation and prevention. 4. Improve hospital level outcomes for different procedures. 5. Predict and plan resource requirements for healthcare emergencies. 6. Design and implement preventive healthcare programs at population levels. 7. Design and plan clinical pathways to reduce wastages and reduce healthcare costs.