Knowledge-driven systems for decision-making in health care applications are powerful tools to help provide actionable and explainable insights to patients and practitioners. In such systems, knowledge about the particular patient - current condition, historical ailments, etc. - is central to enable personalized health care. An example of such a system for personalized health care is a diet and lifestyle decision-making tool for diabetic patients. This system may utilize knowledge from several domain-specific knowledge graphs (KGs), such as a KG of diabetes health care guidelines from the American Diabetes Association and a KG of food and nutrition such as FoodKG [4]. Knowledge about a particular patient is used here to perform context-aware reasoning and personalization of down-stream applications. For example, what the system recommends as a "healthy" meal may differ for among patients based on personal aspects like their current weight or exercise habits. To facilitate reasoning and decision-making based on personal context, such systems can benefit from integrating personal knowledge about the patient. This extended abstract presents a brief review of existing work surrounding the concept of personal knowledge graphs (PKG), how they could be integrated into personalized healthcare as personal health knowledge graphs (PHKG), and the key gaps in existing literature that must be addressed to realize their full potential.