The　prosperity　of　e-health　is　boosted　by　fast　development　of　medical　devices　with　wireless　communications　capability　such　as　wearable　devices,　tiny　sensors,　monitoring　equipments,　etc.,　which　are　randomly　distributed　in　clinic　environments.　The　drastically-increasing　population　of　such　devices　imposes　new　challenges　on　the　limited　wireless　resources.　To　relieve　this　problem,　key　knowledge　needs　to　be　extracted　from　massive　connection　attempts　dispersed　in　the　air　towards　efficient　access　control.　In　this　paper,　a　hybrid　periodic-random　massive　access　(HPRMA)　scheme　for　wireless　clinical　networks　employing　ultra-narrow　band　(UNB)　techniques　is　proposed.　In　particular,　the　proposed　scheme　towards　accommodating　a　large　population　of　devices　include　the　following　new　features.　On　one　hand,　it　can　dynamically　adjust　the　resource　allocated　for　coexisting　periodic　and　random　services　based　on　the　traffic　load　learned　from　signal　collision　status.　On　the　other　hand,　the　resource　allocation　within　periodic　services　is　thoroughly　designed　to　simultaneously　align　with　the　timing　requests　of　differentiated　services.　Abundant　simulation　results　are　also　presented　to　demonstrate　the　superiority　of　the　proposed　HPRMA　scheme　over　baseline　schemes　including　time-division　multiple　access　(TDMA)　and　random　access　approach,　in　terms　of　channel　utilization　efficiency,　packet　drop　ratio,　etc.,　for　the　support　of　massive　devices＇　services.