Connected health and the digital age of medicine

The four bullets escalate deliberately and the grade tracks how far up that escalation you get. Identifying two innovations is description. Describing how they provide data for decision making is explanation. Discussing how real-time data encourages outcome-focused planning is analysis, and predicting future impact with rationale and examples is the argument. Most submissions do the first bullet three times over, filling ten slides with product features and then landing the last two bullets in a sentence each. Budget the slides in advance against the bullets rather than against the products: roughly two slides for the innovations, two or three for the data and decision making, two for outcome-focused planning, and two for the prediction. If a slide is not attached to a bullet, it is probably describing a company rather than answering the assignment. The introduction and reference slides do not count toward the eight to ten, which gives you slightly more room than the number first suggests.

The phrase patients, providers and insurers across the care continuum is the specification your two innovations have to meet, and it rules out a lot of otherwise sensible choices. A tool that helps clinicians alone is not connecting three parties. The connective element is usually the data layer rather than the interface: a molecular profiling and clinical decision platform that reaches the oncologist while the same data supports a coverage determination, or a payer-provider platform that carries eligibility, authorisation and clinical information in the same channel. Say for each innovation who the three parties are and what specifically moves between them. Naming the parties explicitly on the slide is a small move that answers the bullet visibly rather than by implication. Doing so also forces you to check that the innovation really does reach all three, which is where several obvious candidates quietly fail. Where one party is only indirectly involved, say so rather than overstate the connection.

The second bullet is about decision making and the trap is to describe capability instead. A dashboard displaying data does not by itself provide the data necessary for a decision; what matters is which decision, made by whom, at what moment, and what would have happened without the information. Choose two concrete decisions and follow them: a treatment selection where genomic data narrows the options, an authorisation decision that is made in minutes rather than days, a discharge decision that depends on knowing what home support exists. This is also where you can be honest that data supply and decision quality are not the same thing, since more information at the point of care can raise cognitive burden as easily as it lowers it. That observation demonstrates analysis rather than enthusiasm. A decision traced from trigger to action is worth three slides of feature description, and it is the kind of content a marker can actually assess against the bullet.

Real-time and outcome-focused are the two words to unpack in the third bullet. Real-time matters because it changes what an intervention can be: retrospective data supports planning for the next cohort, whereas current data supports acting on this patient now, and continuous monitoring supports acting before an event rather than after. Outcome-focused planning means organising care around results achieved rather than services delivered, which is the reasoning behind value-based contracts, shared savings arrangements and readmission penalties. Tie the two together explicitly: real-time data is what makes outcome-based arrangements administrable, because a party held accountable for an outcome needs to see it moving rather than learn about it in a quarterly report. That connection is the analytical core of the whole presentation. Without it, an insurer is settling accounts after the fact and the arrangement becomes a retrospective audit rather than a way of managing care. Say that plainly on the slide.

The prediction bullet asks for rationale and examples, which means a forecast that could be wrong and evidence for why it might be right. Anchor it in something already observable rather than in generalities about transformation: the shift of monitoring into the home, algorithmic decision support moving from suggestion to default, payment models that pay for a period of care rather than an encounter, or interoperability requirements changing what data a patient can take with them. For each, say what evidence points that way and what would have to happen for it to stall — reimbursement that does not follow, workforce that cannot absorb the alerts, or trust that does not survive an error. A prediction with a stated failure condition is far more persuasive than one without. Naming what would stop each trend is also the fastest way to show you are reasoning rather than repeating industry copy, and it costs one line per prediction.

On execution, the formatting instructions are unusually specific and they are gradeable: eight to ten slides excluding introduction and references, speaker notes on every slide, clean and uncluttered slides with the detail in the notes, in-text citations used, a minimum of three academic sources from credible sources, APA throughout, and a similarity index that must stay under fifteen percent. That last constraint is worth planning for rather than discovering, and the reliable way to meet it is to write the notes in your own words from sources you have read rather than assembling quotations. Cite on the slide as well as in the notes where a claim is not yours. And treat the company's own material as a primary source about its products but not as evidence for claims about outcomes, which need independent literature. Read the notes aloud once before submitting, since notes written to be read silently usually run too long for the time a presentation allows.